SaaS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be219bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "pd.options.display.float_format = '{:,.0f}'.format\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa3da185",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('/Users/brendan/Desktop/meritech_table.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20471655",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['% Price 3-Month'] = df['% Price 3-Month']* 100\n",
    "df['% Price 12 Month'] = df['% Price 12 Month']*100\n",
    "df['Implied ARR % YoY'] = df['Implied ARR % YoY']*100\n",
    "df['LTM Revenue % YoY'] = df['LTM Revenue % YoY'] *100\n",
    "df['NTM Revenue % YoY'] = df['NTM Revenue % YoY']*100\n",
    "df['GM % (LTM)'] = df['GM % (LTM)']*100\n",
    "df['S&M (% LTM)'] = df['S&M (% LTM)']*100\n",
    "df['R&D (% LTM)'] = df['R&D (% LTM)']*100\n",
    "df['G&A (% LTM)'] = df['G&A (% LTM)']*100\n",
    "df['Opex (% LTM)'] = df['Opex (% LTM)']*100\n",
    "df['FCF (% LTM)'] = df['FCF (% LTM)']*100\n",
    "df['Rule of 40'] = df['Rule of 40']*100\n",
    "df['Net Dollar Retention'] = df['Net Dollar Retention']*100\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd536a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Price', '% Price 3-Month', '% Price 12 Month', 'Equity Value',\n",
       "       'Enterprise Value', 'EV / Implied ARR', 'EV / NTM Revenue',\n",
       "       'Growth Adj. EV / NTM Revenue', 'Implied ARR', 'Net New ARR',\n",
       "       'LTM Revenue', 'Implied ARR % YoY', 'LTM Revenue % YoY',\n",
       "       'NTM Revenue % YoY', 'GM % (LTM)', 'S&M (% LTM)', 'R&D (% LTM)',\n",
       "       'G&A (% LTM)', 'Opex (% LTM)', 'FCF (% LTM)', 'Rule of 40',\n",
       "       'Magic Number', 'Payback Period', 'Implied Avg ACV', 'Implied ARR/FTE',\n",
       "       'Annualized OpEx/FTE', 'Net Dollar Retention',\n",
       "       'Mult. Return since IPO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[3:].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "741f8b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                             object\n",
       "Price                           float64\n",
       "% Price 3-Month                 float64\n",
       "% Price 12 Month                 object\n",
       "Equity Value                    float64\n",
       "Enterprise Value                float64\n",
       "EV / Implied ARR                 object\n",
       "EV / NTM Revenue                float64\n",
       "Growth Adj. EV / NTM Revenue     object\n",
       "Implied ARR                     float64\n",
       "Net New ARR                     float64\n",
       "LTM Revenue                     float64\n",
       "Implied ARR % YoY               float64\n",
       "LTM Revenue % YoY               float64\n",
       "NTM Revenue % YoY               float64\n",
       "GM % (LTM)                      float64\n",
       "S&M (% LTM)                     float64\n",
       "R&D (% LTM)                     float64\n",
       "G&A (% LTM)                      object\n",
       "Opex (% LTM)                    float64\n",
       "FCF (% LTM)                     float64\n",
       "Rule of 40                      float64\n",
       "Magic Number                     object\n",
       "Payback Period                   object\n",
       "Implied Avg ACV                  object\n",
       "Implied ARR/FTE                 float64\n",
       "Annualized OpEx/FTE             float64\n",
       "Net Dollar Retention            float64\n",
       "Mult. Return since IPO           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = df[3:]\n",
    "df_.columns\n",
    "df_.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b495df0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "881280f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_.plot(x='NTM Revenue % YoY',y='EV / NTM Revenue', kind='scatter')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c245eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_['NTM Revenue % YoY']\n",
    "y = df_['EV / NTM Revenue']\n",
    "\n",
    "sns.regplot(x=x, y=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "52209589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = df_['NTM Revenue % YoY']\n",
    "y = df_['EV / NTM Revenue']\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "\n",
    "plt.xlim(0,65)\n",
    "plt.ylim(0,22)\n",
    "\n",
    "plt.scatter(x,y, marker=\"o\",)\n",
    "\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "plt.plot(x, m*x + b)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7782c187",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_['NTM Revenue % YoY']\n",
    "y = df_['EV / NTM Revenue']\n",
    "\n",
    "p = figure(width=800, height=400, tools=\"reset, hover, zoom_in, wheel_zoom, pan, save\")\n",
    "\n",
    "# add a circle renderer with a size, color, and alpha\n",
    "p.circle(x, y, size=20, color=\"navy\", alpha=0.2)\n",
    "\n",
    "# show the results\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cb0ece20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=5bb68d90-bfe5-4a4e-bb8b-4686ec3da51e style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('5bb68d90-bfe5-4a4e-bb8b-4686ec3da51e').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Price</th>\n",
       "      <th>% Price 3-Month</th>\n",
       "      <th>% Price 12 Month</th>\n",
       "      <th>Equity Value</th>\n",
       "      <th>Enterprise Value</th>\n",
       "      <th>EV / Implied ARR</th>\n",
       "      <th>EV / NTM Revenue</th>\n",
       "      <th>Growth Adj. EV / NTM Revenue</th>\n",
       "      <th>Implied ARR</th>\n",
       "      <th>...</th>\n",
       "      <th>Opex (% LTM)</th>\n",
       "      <th>FCF (% LTM)</th>\n",
       "      <th>Rule of 40</th>\n",
       "      <th>Magic Number</th>\n",
       "      <th>Payback Period</th>\n",
       "      <th>Implied Avg ACV</th>\n",
       "      <th>Implied ARR/FTE</th>\n",
       "      <th>Annualized OpEx/FTE</th>\n",
       "      <th>Net Dollar Retention</th>\n",
       "      <th>Mult. Return since IPO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adobe</td>\n",
       "      <td>286</td>\n",
       "      <td>-27</td>\n",
       "      <td>-50</td>\n",
       "      <td>132,831</td>\n",
       "      <td>131,194</td>\n",
       "      <td>7.4x</td>\n",
       "      <td>7</td>\n",
       "      <td>0.59x</td>\n",
       "      <td>17,732</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>-</td>\n",
       "      <td>633</td>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alkami</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>-39</td>\n",
       "      <td>1,460</td>\n",
       "      <td>1,331</td>\n",
       "      <td>6.6x</td>\n",
       "      <td>6</td>\n",
       "      <td>0.20x</td>\n",
       "      <td>202</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>-21</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>522</td>\n",
       "      <td>254</td>\n",
       "      <td>169</td>\n",
       "      <td>115</td>\n",
       "      <td>0.5x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "     Name  Price  % Price 3-Month % Price 12 Month  Equity Value  \\\n",
       "3   Adobe    286              -27              -50       132,831   \n",
       "4  Alkami     15                6              -39         1,460   \n",
       "\n",
       "   Enterprise Value EV / Implied ARR  EV / NTM Revenue  \\\n",
       "3           131,194             7.4x                 7   \n",
       "4             1,331             6.6x                 6   \n",
       "\n",
       "  Growth Adj. EV / NTM Revenue  Implied ARR  ...  Opex (% LTM)  FCF (% LTM)  \\\n",
       "3                        0.59x       17,732  ...            44           41   \n",
       "4                        0.20x          202  ...            68          -21   \n",
       "\n",
       "   Rule of 40  Magic Number  Payback Period  Implied Avg ACV  Implied ARR/FTE  \\\n",
       "3          54             0              83                -              633   \n",
       "4          17             3               7              522              254   \n",
       "\n",
       "   Annualized OpEx/FTE Net Dollar Retention  Mult. Return since IPO  \n",
       "3                  279                    0                       0  \n",
       "4                  169                  115                    0.5x  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3df644a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                             object\n",
       "Price                           float64\n",
       "% Price 3-Month                 float64\n",
       "% Price 12 Month                 object\n",
       "Equity Value                    float64\n",
       "Enterprise Value                float64\n",
       "EV / Implied ARR                 object\n",
       "EV / NTM Revenue                float64\n",
       "Growth Adj. EV / NTM Revenue     object\n",
       "Implied ARR                     float64\n",
       "Net New ARR                     float64\n",
       "LTM Revenue                     float64\n",
       "Implied ARR % YoY               float64\n",
       "LTM Revenue % YoY               float64\n",
       "NTM Revenue % YoY               float64\n",
       "GM % (LTM)                      float64\n",
       "S&M (% LTM)                     float64\n",
       "R&D (% LTM)                     float64\n",
       "G&A (% LTM)                      object\n",
       "Opex (% LTM)                    float64\n",
       "FCF (% LTM)                     float64\n",
       "Rule of 40                      float64\n",
       "Magic Number                     object\n",
       "Payback Period                   object\n",
       "Implied Avg ACV                  object\n",
       "Implied ARR/FTE                 float64\n",
       "Annualized OpEx/FTE             float64\n",
       "Net Dollar Retention            float64\n",
       "Mult. Return since IPO           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed6c6f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.transform import factor_cmap, factor_mark\n",
    "\n",
    "SPECIES = sorted(data.species.unique())\n",
    "MARKERS = ['hex', 'circle_x', 'triangle']\n",
    "\n",
    "p = figure(title = \"Penguin size\", background_fill_color=\"#fafafa\")\n",
    "p.xaxis.axis_label = 'Flipper Length (mm)'\n",
    "p.yaxis.axis_label = 'Body Mass (g)'\n",
    "\n",
    "p.scatter(\"flipper_length_mm\", \"body_mass_g\", source=data,\n",
    "          legend_group=\"species\", fill_alpha=0.4, size=12,\n",
    "          marker=factor_mark('species', MARKERS, SPECIES),\n",
    "          color=factor_cmap('species', 'Category10_3', SPECIES))\n",
    "\n",
    "p.legend.location = \"top_left\"\n",
    "p.legend.title = \"Species\"\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "118ff31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_['NTM Revenue % YoY']\n",
    "y = df_['EV / NTM Revenue']\n",
    "company = df_['Name']\n",
    "size = df_['Equity Value']\n",
    "ev = df_['Enterprise Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d16107df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"17549\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  const force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    const script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    const cell = handle.cell;\n",
       "\n",
       "    const id = cell.output_area._bokeh_element_id;\n",
       "    const server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd_clean, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            const id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd_destroy);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    const output_area = handle.output_area;\n",
       "    const output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      const bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      const script_attrs = bk_div.children[0].attributes;\n",
       "      for (let i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      const toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    const events = require('base/js/events');\n",
       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    const el = document.getElementById(\"17549\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n",
       "  const css_urls = [];\n",
       "  \n",
       "\n",
       "  const inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"17549\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"17549\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n  const css_urls = [];\n  \n\n  const inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"17549\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"4fa00815-518b-4ca7-a429-20f8a1ca66d9\" data-root-id=\"17550\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  const docs_json = {\"9170f063-13bf-4f70-a7b1-91b301230b21\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"17561\"}],\"center\":[{\"id\":\"17564\"},{\"id\":\"17568\"}],\"height\":800,\"left\":[{\"id\":\"17565\"}],\"renderers\":[{\"id\":\"17577\"}],\"title\":{\"id\":\"17551\"},\"toolbar\":{\"id\":\"17569\"},\"width\":800,\"x_range\":{\"id\":\"17553\"},\"x_scale\":{\"id\":\"17557\"},\"y_range\":{\"id\":\"17555\"},\"y_scale\":{\"id\":\"17559\"}},\"id\":\"17550\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"data\":{\"company\":[\"Adobe\",\"Alkami\",\"Alteryx\",\"*Amplitude\",\"AppFolio\",\"Asana\",\"Atlassian\",\"Avalara\",\"BigCommerce\",\"Bill.com\",\"BlackLine\",\"Blend\",\"Box\",\"*Braze\",\"C3.ai\",\"Clearwater Analytics\",\"Cloudflare\",\"Confluent\",\"Couchbase\",\"Coupa\",\"CrowdStrike\",\"CS Disco\",\"Cvent\",\"CyberArk\",\"*Datadog\",\"DigitalOcean\",\"DocuSign\",\"Domo\",\"Doximity\",\"Dropbox\",\"Elastic\",\"Fastly\",\"Five9\",\"*ForgeRock\",\"Freshworks\",\"GitLab\",\"HashiCorp\",\"HubSpot\",\"Instructure\",\"Jamf\",\"*JFrog\",\"KnowBe4\",\"Lightspeed POS\",\"Momentive Global\",\"Monday.com\",\"MongoDB\",\"nCino\",\"New Relic\",\"Okta\",\"Olo\",\"PagerDuty\",\"Palantir\",\"Paycom\",\"Paylocity\",\"Ping Identity\",\"Procore\",\"Q2\",\"Qualtrics\",\"Qualys\",\"Rapid7\",\"RingCentral\",\"Salesforce\",\"Samsara\",\"SentinelOne\",\"ServiceNow\",\"Shopify\",\"Smartsheet\",\"*Snowflake\",\"Sprinklr\",\"Sprout\",\"Squarespace\",\"Sumo 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Revenue Growth v EV/NTM Revenue, scaled by Market Cap\"},\"id\":\"17551\",\"type\":\"Title\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"field\":\"size\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"17574\",\"type\":\"Circle\"},{\"attributes\":{\"axis_label\":\"EV / NTM Revenue\",\"coordinates\":null,\"formatter\":{\"id\":\"18040\"},\"group\":null,\"major_label_policy\":{\"id\":\"18041\"},\"ticker\":{\"id\":\"17566\"}},\"id\":\"17565\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"17555\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"18045\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"18046\",\"type\":\"Selection\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"value\":\"#1f77b4\"},\"hatch_alpha\":{\"value\":0.2},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"field\":\"size\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"17576\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"17566\",\"type\":\"BasicTicker\"},{\"attributes\":{\"callback\":null,\"tooltips\":[[\"Company\",\"@company\"],[\"NTM Revenue Growth % \",\"$x\"],[\"EV/NTM Revenue\",\"$y\"],[\"Enterprise Value ($bn): 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       "  const render_items = [{\"docid\":\"9170f063-13bf-4f70-a7b1-91b301230b21\",\"root_ids\":[\"17550\"],\"roots\":{\"17550\":\"4fa00815-518b-4ca7-a429-20f8a1ca66d9\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "17550"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file, show, ColumnDataSource\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.palettes import Viridis256 as palette\n",
    "from bokeh.transform import linear_cmap\n",
    "output_notebook()\n",
    "\n",
    "\n",
    "\n",
    "p = figure(plot_width=800, plot_height=800, tools=[hover],\n",
    "           title=\"Expected Revenue Growth v EV/NTM Revenue, scaled by Market Cap\")\n",
    "# Defining the X-Axis Label\n",
    "p.xaxis.axis_label = \"NTM Revenue % YoY\"\n",
    " \n",
    "# defining the Y-Axis Label\n",
    "p.yaxis.axis_label = 'EV / NTM Revenue'\n",
    "\n",
    "source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            x=x,\n",
    "            y=y,\n",
    "            company=company,\n",
    "            size = size/1000\n",
    "        )\n",
    "    )\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"Company\", \"@company\"),\n",
    "            (\"NTM Revenue Growth % \", \"$x\"),\n",
    "            (\"EV/NTM Revenue\", \"$y\"),\n",
    "            ('Enterprise Value ($bn): ', '$size')\n",
    "            \n",
    "        ]\n",
    "    )\n",
    "\n",
    "p.circle(x='x', y='y', size='size', source=source)\n",
    "show(p)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51764069",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c8ba5bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f9e742d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"8079b569-0f11-406a-b63d-dce045c2cfda\" data-root-id=\"14000\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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       "  const render_items = [{\"docid\":\"1d7523c2-2a35-46d8-9d63-5b6c03281648\",\"root_ids\":[\"14000\"],\"roots\":{\"14000\":\"8079b569-0f11-406a-b63d-dce045c2cfda\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "14000"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=800, plot_height=800, tools=[hover],\n",
    "           title=\"Expected Revenue Growth v EV/NTM Revenue, scaled by Market Cap\")\n",
    "# Defining the X-Axis Label\n",
    "p.xaxis.axis_label = \"NTM Revenue % YoY\"\n",
    " \n",
    "# defining the Y-Axis Label\n",
    "p.yaxis.axis_label = 'EV / NTM Revenue'\n",
    "\n",
    "source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            x=x,\n",
    "            y=y,\n",
    "            company=company,\n",
    "            size = size/1000\n",
    "        )\n",
    "    )\n",
    "\n",
    "colormapper = linear_cmap(field_name = 'size', palette=palette, low=min(df_['Equity Value']), high=max(df_['Equity Value']))\n",
    "\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"Company\", \"@company\"),\n",
    "            (\"NTM Revenue Growth % \", \"$x\"),\n",
    "            (\"EV/NTM Revenue\", \"$y\"),\n",
    "            ('Enterprise Value ($bn): ', '$size')\n",
    "            \n",
    "        ]\n",
    "    )\n",
    "\n",
    "p.circle(x='x', y='y', size='size', source=source, color= colormapper)\n",
    "show(p)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edcc626d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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