Plotly-Pandas-Datetime-TrekStocks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will use my created Star Trek Stocks data to simulate the NASDAQ stockmarket \n",
    "financial data often used in copious tutorials\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Title: Star Trek Stocks & various code\n",
    "- Zane Dax\n",
    "- Nov 14, 2020\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7613  4817  7530  7800  3445  9445  7377  3138 10229 10725]\n",
      "[18457 13852 18075  9860  3133 12914 14385 17133  4021 13536]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# np.random.randint(1e3,2e4,100)\n",
    "x =  np.random.randint(1e3,2e4,10)\n",
    "#[int(i) for i in str(x)]\n",
    "print(x)\n",
    "\n",
    "m = np.random.randint(1e3,2e4,10)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7613, 4817, 7530, 7800, 3445, 9445, 7377, 3138, 10229, 10725] \n",
      "\n",
      "[18457, 13852, 18075, 9860, 3133, 12914, 14385, 17133, 4021, 13536]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = []\n",
    "k = []\n",
    "\n",
    "for i in x:\n",
    "    l.append(i)\n",
    "    \n",
    "for n in m:\n",
    "    k.append(n)\n",
    "    #print(i)\n",
    "    \n",
    "    \n",
    "print(l,'\\n')\n",
    "print(k)\n",
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26070, 18669, 25605, 17660,  6578, 22359, 21762, 20271, 14250,\n",
       "       24261])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type(l)\n",
    "u = l + m\n",
    "u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7613-7530\n"
     ]
    }
   ],
   "source": [
    "y = x[0] \n",
    "y2= x[2]\n",
    "\n",
    "print(\"{}-{}\".format(y,y2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Promenade</th>\n",
       "      <th>QuarksBar</th>\n",
       "      <th>CargoBays</th>\n",
       "      <th>ShuttleBay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Promenade  QuarksBar  CargoBays  ShuttleBay\n",
       "0          3          1          4           8\n",
       "1          0          9          9           1\n",
       "2          0          6          6           1\n",
       "3          7          6          9           7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DS9 crime rate\n",
    "\n",
    "ds9_crime = pd.DataFrame(np.random.randint(0,10, (4,4)),\n",
    "                          columns=['Promenade',\n",
    "                                   'QuarksBar',\n",
    "                                   'CargoBays',\n",
    "                                   'ShuttleBay'] )\n",
    "\n",
    "ds9_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2231-07-03', '2231-07-04', '2231-07-05', '2231-07-06',\n",
       "               '2231-07-07', '2231-07-08', '2231-07-09', '2231-07-10',\n",
       "               '2231-07-11', '2231-07-12'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "panda_d = pd.to_datetime(\"3rd of July, 2231\") \n",
    "panda_d = panda_d + pd.to_timedelta(np.arange(10), 'D')\n",
    "panda_d"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "stock_values = pd.Series({\n",
    "    'TSX': np.random(0,1e6),\n",
    "    \n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSX</th>\n",
       "      <th>ASX</th>\n",
       "      <th>BSX</th>\n",
       "      <th>RSX</th>\n",
       "      <th>REX</th>\n",
       "      <th>VMX.V</th>\n",
       "      <th>BNXR</th>\n",
       "      <th>CRXM</th>\n",
       "      <th>DRX.L</th>\n",
       "      <th>ENTX</th>\n",
       "    </tr>\n",
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       "      <th>2231-07-03</th>\n",
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       "      <th>2231-07-05</th>\n",
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       "      <th>2231-07-08</th>\n",
       "      <td>424057399</td>\n",
       "      <td>498639062</td>\n",
       "      <td>83711641</td>\n",
       "      <td>66884905</td>\n",
       "      <td>181830616</td>\n",
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       "      <td>132660456</td>\n",
       "      <td>399157363</td>\n",
       "      <td>449265514</td>\n",
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       "    <tr>\n",
       "      <th>2231-07-09</th>\n",
       "      <td>189484999</td>\n",
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       "      <td>55533015</td>\n",
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       "      <td>9272046</td>\n",
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      "text/plain": [
       "                  TSX        ASX        BSX        RSX        REX      VMX.V  \\\n",
       "2231-07-03  434523095  108382095  357475196  180544360  297491943   27382953   \n",
       "2231-07-04  197005101   60506796  213264945  107283665  412472387  220245938   \n",
       "2231-07-05  370406838   97630712  150504034  177397946  214418716   14056564   \n",
       "2231-07-06  245171001  367956925  340538984  220134185  345793740  152908866   \n",
       "2231-07-07  242818607   24195727  267498228  284727982  293640429  119026220   \n",
       "2231-07-08  424057399  498639062   83711641   66884905  181830616  404121256   \n",
       "2231-07-09  189484999  358874832   55533015  423101700  374935739  219289330   \n",
       "2231-07-10    9272046  325889586  374477033  415353141  220236534  366408850   \n",
       "2231-07-11   81035425  429989250   90538974  102169541  215761175  413350621   \n",
       "2231-07-12  257127693   69760786  136730587  464236238   88337804   66394129   \n",
       "\n",
       "                 BNXR       CRXM      DRX.L       ENTX  \n",
       "2231-07-03  375525010  487861779  346663966   81891488  \n",
       "2231-07-04  353495636  116940025   53502013  139382411  \n",
       "2231-07-05  135821368  320375279   12429650   69681018  \n",
       "2231-07-06   41819912  199913935  295798439  396124416  \n",
       "2231-07-07  423698146    3769014  417986183  189517458  \n",
       "2231-07-08  332017062  132660456  399157363  449265514  \n",
       "2231-07-09  320573889  279559761  262442455  202454174  \n",
       "2231-07-10  435416197  270530913   19480528  421186393  \n",
       "2231-07-11   70884382  204871931  454423716  371526208  \n",
       "2231-07-12  235350460    8003581  345550879  407377168  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "trek_stocks = pd.DataFrame(\n",
    "    np.random.randint(1e6,5e8,(10,10)),\n",
    "    panda_d,\n",
    "    columns=['TSX','ASX','BSX',\n",
    "             'RSX','REX','VMX.V',\n",
    "             'BNXR','CRXM','DRX.L', \n",
    "             'ENTX'],\n",
    "#     index=['Date',panda_d]\n",
    "   )\n",
    "\n",
    "trek_stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Function to plot interactive plot\n",
    "def plotly_plot(df, title):\n",
    "    \n",
    "    fig = px.line(title = title)\n",
    "    fig.layout.template = 'plotly_dark'\n",
    "    for i in df.columns[1:]:\n",
    "        fig.add_scatter(x = df['TSX'], y = df[i], name = i)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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\"rgb(17,17,17)\", \"borderwidth\": 1, \"tickwidth\": 0}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"#506784\", \"linecolor\": \"#506784\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"#506784\", \"linecolor\": \"#506784\", \"ticks\": \"\"}, \"bgcolor\": \"rgb(17,17,17)\", \"caxis\": {\"gridcolor\": \"#506784\", \"linecolor\": \"#506784\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"updatemenudefaults\": {\"bgcolor\": \"#506784\", \"borderwidth\": 0}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"#283442\", \"linecolor\": \"#506784\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"#283442\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"#283442\", \"linecolor\": \"#506784\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"#283442\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Plotly: Trek Stocks\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('74cbb8a1-e68c-4d0c-90c1-bc97586c82e5');\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"
    }
   ],
   "source": [
    "# Plot interactive chart\n",
    "plotly_plot(trek_stocks, 'Plotly: Trek Stocks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "(BNXR)for Financial Time Series to be fun, think of \n",
    "The Ferengi Market Exchange was a center of Ferengi commerce throughout the Alpha Quadrant. commodities, such as latinum and duranium, and securities.\n",
    "\n",
    "The Exchange was viewed as a significant indicator of the overall financial health of the Ferengi Alliance. Consequently, a drastic change in the Exchange could have political as well as economic repercussions within the Alliance. Grand Nagus Smeet, the only Nagus ever assassinated, oversaw the greatest drop in the value of the Exchange prior to 2373 when a single-day drop of 199 points nearly caused the overthrow of Grand Nagus Zek.\n",
    "\n",
    "## exoplanetary stock market:\n",
    "Trill Stock Exchange (TSX)\n",
    "Andoria (ASX)\n",
    "Bolarus IX (BSX)\n",
    "Risa (RSX)\n",
    "Remus (REX)\n",
    "Valt Minor (VMX.V)\n",
    "Benthans (BNXR)\n",
    "Cravic (CRXM)\n",
    "Drayan (DRX.L)\n",
    "Entharans (ENTX)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "s = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies#S&P_500_component_stocks')\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>YUM</td>\n",
       "      <td>Yum! Brands Inc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>Louisville, Kentucky</td>\n",
       "      <td>1997-10-06</td>\n",
       "      <td>1041061</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ZBRA</td>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Electronic Equipment &amp; Instruments</td>\n",
       "      <td>Lincolnshire, Illinois</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>877212</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>ZBH</td>\n",
       "      <td>Zimmer Biomet</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Warsaw, Indiana</td>\n",
       "      <td>2001-08-07</td>\n",
       "      <td>1136869</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>ZION</td>\n",
       "      <td>Zions Bancorp</td>\n",
       "      <td>reports</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Regional Banks</td>\n",
       "      <td>Salt Lake City, Utah</td>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>109380</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>ZTS</td>\n",
       "      <td>Zoetis</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>Florham Park, New Jersey</td>\n",
       "      <td>2013-06-21</td>\n",
       "      <td>1555280</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol            Security SEC filings             GICS Sector  \\\n",
       "500    YUM     Yum! Brands Inc     reports  Consumer Discretionary   \n",
       "501   ZBRA  Zebra Technologies     reports  Information Technology   \n",
       "502    ZBH       Zimmer Biomet     reports             Health Care   \n",
       "503   ZION       Zions Bancorp     reports              Financials   \n",
       "504    ZTS              Zoetis     reports             Health Care   \n",
       "\n",
       "                      GICS Sub-Industry     Headquarters Location  \\\n",
       "500                         Restaurants      Louisville, Kentucky   \n",
       "501  Electronic Equipment & Instruments    Lincolnshire, Illinois   \n",
       "502               Health Care Equipment           Warsaw, Indiana   \n",
       "503                      Regional Banks      Salt Lake City, Utah   \n",
       "504                     Pharmaceuticals  Florham Park, New Jersey   \n",
       "\n",
       "    Date first added      CIK Founded  \n",
       "500       1997-10-06  1041061    1997  \n",
       "501       2019-12-23   877212    1969  \n",
       "502       2001-08-07  1136869    1927  \n",
       "503       2001-06-22   109380    1873  \n",
       "504       2013-06-21  1555280    1952  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = s[0] \n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df = df.to_csv('S&P_Stocks.csv', index=False) # index=False IMPORTANT!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What's special about 277777788888899?\n",
    "\n",
    "# 5428  => (5*2)*(4*8)\n",
    "# 327\n",
    "x = 3*2 #5*2 \n",
    "y = x*7 #4*8\n",
    "y\n",
    "\n",
    "\n",
    "#z = x * y\n",
    "#z\n",
    "\n",
    "\n",
    "t = 4 #3\n",
    "w = 2\n",
    "#j = 0\n",
    "h = t*w#*j\n",
    "h\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that finds  multiplicative persistence.\n",
    "\n",
    "def mult_persist(n, steps=0):\n",
    "    if len(str(n)) ==1:\n",
    "        print('result {}'.format(n))\n",
    "        print(\"Total steps: \", str(steps))\n",
    "        return 99\n",
    "\n",
    "    steps +=1\n",
    "    digits = [int(i) for i in str(n)]\n",
    "    result = 1\n",
    "    for j in digits:\n",
    "        result *=j\n",
    "        print(j,':',result,'...')\n",
    "\n",
    "    mult_persist(result,steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 : 9 ...\n",
      "9 : 81 ...\n",
      "9 : 729 ...\n",
      "9 : 6561 ...\n",
      "6 : 6 ...\n",
      "5 : 30 ...\n",
      "6 : 180 ...\n",
      "1 : 180 ...\n",
      "1 : 1 ...\n",
      "8 : 8 ...\n",
      "0 : 0 ...\n",
      "result 0\n",
      "Total steps:  3\n"
     ]
    }
   ],
   "source": [
    "mult_persist(9999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## parker square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>81</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       one  two  three\n",
       "one     81   16     36\n",
       "two      9   16     25\n",
       "three   25    9     25"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "random_state = 100\n",
    "n0= np.random.randint(2,10)\n",
    "n1 = np.random.randint(2,10)\n",
    "n2 = np.random.randint(2,10)\n",
    "n3 = np.random.randint(2,10)\n",
    "n4 = np.random.randint(2,10)\n",
    "n5 = np.random.randint(2,10)\n",
    "n6 = np.random.randint(2,10)\n",
    "n7 = np.random.randint(2,10)\n",
    "n8 = np.random.randint(2,10)\n",
    "#n9 = np.random.randint(2,10)\n",
    "\n",
    "p = pd.DataFrame({\n",
    "    'one': [n0**2, n1**2, n2**2],\n",
    "    'two': [n3**2, n4**2, n5**2],\n",
    "    'three': [n6**2, n7**2, n8**2]\n",
    "    },\n",
    "    index=['one','two','three']\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column 1 sum</th>\n",
       "      <th>Column 2 sum</th>\n",
       "      <th>Column 3 sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row 1 sum</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row 2 sum</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row 3 sum</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Column 1 sum  Column 2 sum  Column 3 sum\n",
       "row 1 sum            19            11            14\n",
       "row 2 sum            19            11            14\n",
       "row 3 sum            19            11            14"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(n0,n3,n6)\n",
    "# print(n8,n4,n7)\n",
    "# print(n2,n5,n1)\n",
    "\n",
    "#\n",
    "\n",
    "col_1 = [n0,n8,n2]\n",
    "col_1_1 = [col_1[0] + col_1[1] + col_1[2]]\n",
    "# print('column 1 sum :',col_1_1)\n",
    "\n",
    "col_2 = [n3,n4,n5]\n",
    "col_2_2 = [col_2[0] + col_2[1] + col_2[2]]\n",
    "# print('column 2 sum: ',col_2_2)\n",
    "\n",
    "col_3 = [n6,n7,n1]\n",
    "col_3_3 = [col_3[0] + col_3[1] + col_3[2]]\n",
    "# print('column 3 sum: ',col_3_3)\n",
    "\n",
    "#=============================\n",
    "row_1 = [n0,n3,n6]\n",
    "row_1_1 = [row_1[0] + row_1[1] + row_1[2]]\n",
    "# print('row 1 sum :',row_1_1)\n",
    "\n",
    "row_2 = [n8,n4,n7]\n",
    "row_2_2 = [row_2[0] + row_2[1] + row_2[2]]\n",
    "# print('row 2 sum: ',row_2_2)\n",
    "\n",
    "row_3 = [n2,n5,n1]\n",
    "row_3_3 = [row_3[0] + row_3[1] + row_3[2]]\n",
    "# print('row 2 sum: ',row_3_3)\n",
    "\n",
    "# diagonal sum\n",
    "diag_L2R = [n0,n4,n1]\n",
    "diag_L2R_sum = [diag_L2R[0] + diag_L2R[1] + diag_L2R[2]]\n",
    "# print(\"diagonal L to R (top-down): \",diag_L2R_sum )\n",
    "\n",
    "# diagonal sum\n",
    "diag_R2L = [n2,n4,n6]\n",
    "diag_R2L_sum = [diag_R2L[0] + diag_R2L[1] + diag_R2L[2]]\n",
    "# print(\"diagonal R to L (down-up): \",diag_R2L_sum )\n",
    "\n",
    "\n",
    "\n",
    "sqr= pd.DataFrame(\n",
    "    {'Column 1 sum': col_1_1,\n",
    "     'Column 2 sum': col_2_2,\n",
    "     'Column 3 sum': col_3_3},\n",
    "    {'row 1 sum': row_1_1,\n",
    "     'row 2 sum': row_2_2,\n",
    "     'row 3 sum': row_3_3},\n",
    "#      index=[diag_L2R_sum ,diag_R2L_sum ]\n",
    "    \n",
    ")\n",
    "sqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.25"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# periodic \n",
    "c = -29/16\n",
    "z = -7/4\n",
    "zee = (z**2) + c\n",
    "zee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 22 28  3  8 29 23 27  9 27  6 20 22 11 25  3 21  6 14 13 11  7  3 11\n",
      "  5 13  9 25  8 21  6 26  5 13 13 19  7 12 12  8  3 28 28  3  6 27 26  8\n",
      " 12 19 20 18  5 11 19 11  3 27 21 15 24 15  8  7  3 17 19 13 25  4 24 18\n",
      "  7 18 27 16  9 20  4 24  4  3 17  4  7  7  4  4 21  8 10 25  5 13 23 11\n",
      " 13 28 29 18] \n",
      " [17 41 53  3 13 55 43 51 15 51  9 37 41 19 47  3 39  9 25 23 19 11  3 19\n",
      "  7 23 15 47 13 39  9 49  7 23 23 35 11 21 21 13  3 53 53  3  9 51 49 13\n",
      " 21 35 37 33  7 19 35 19  3 51 39 27 45 27 13 11  3 31 35 23 47  5 45 33\n",
      " 11 33 51 29 15 37  5 45  5  3 31  5 11 11  5  5 39 13 17 47  7 23 43 19\n",
      " 23 53 55 33]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1.75"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# f(x) = 2x +1   Mersenne Square\n",
    "x = np.random.randint(3,30,100)\n",
    "m = (2 * x) +1\n",
    "m\n",
    "\n",
    "# f(x) = 2x+3\n",
    "x = np.random.randint(3,30,100)\n",
    "m = (2 * x) - 3\n",
    "print(x,'\\n',m)\n",
    "\n",
    "# f(x)= 2x -7/4\n",
    "x = 0\n",
    "m = (x**2) - 7/4\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4900  PYRAMID SQUARE: n(n+1)(2n+1)/6 = m^2   \n",
    "# canon ball stacking pyramid\n",
    "# both square AND a pyramid\n",
    "\n",
    "# tree(3)\n",
    "# node1, node2, node3\n",
    "\n",
    "# sum to infinity, n=0 Sigma 1/2x\n",
    "#  make 7 even => remove the 's'\n",
    "# what 0 told 8, nice belt!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([366, 300, 247,  64,  92, 103, 326,  63, 270, 237])"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = np.random.randint(20, 400, 10)\n",
    "n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "highly composite number (antiprime)\n",
    "more factor or divisors than smaller than it d(n)\n",
    "\n",
    "5040 = 60 factors (1,2,...10)\n",
    "\n",
    "   n: 1 2 3 4 5 6 7 8 9 10 11 12 \n",
    "d(n): 1 2 2 3 2 4 2 2 3 4   2  6\n",
    "\n",
    "p = prime\n",
    "factors of n = pow(p1,1), pow(p2,2) ... pow(pk,n)\n",
    "d(n) = (a1 +1) * (a2 +1) *(a3+1)\n",
    "  \n",
    "powers decrease and end at 1\n",
    "5040 = 2^4 * 3^2 * 5^1 * 7^1\n",
    "\n",
    "d(60)= 2^2 * 3 * 5\n",
    "except: 4=2^2, 36=2^2*3^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "550\n",
      "5040\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "n = pow(2,2) * 3 * 5\n",
    "print(n)\n",
    "n1 = pow(5,2) * 2 * 11\n",
    "print(n1)\n",
    "n2 = pow(2,4) * pow(3,2) * pow(5,1) * pow(7,1)\n",
    "print(n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43046721"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n =pow(3,2)\n",
    "n1 =pow(n,2)\n",
    "n2=pow(n1,2)\n",
    "n3=pow(n2,2)\n",
    "n3"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "n =[1,2,3,4,5,6,7,8,9]\n",
    "for i in n:\n",
    "    m =pow(i,2)\n",
    "    n.append(m)\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for $1 interest for 52 weeks is $2.71\n"
     ]
    }
   ],
   "source": [
    "# interest, Bernoulli formula\n",
    "\n",
    "# money amount\n",
    "money = 1\n",
    "\n",
    "# n = days (365=year), weeks (52=year) months (12), year\n",
    "# n = 52\n",
    "n =365\n",
    "\n",
    "# interest formula\n",
    "f = (1 + (1/n))\n",
    "formula = pow(f, n)\n",
    "\n",
    "interest = money * formula\n",
    "print(\"for ${} interest for 52 weeks is ${:.2f}\".format(1,interest)) "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Euler took on the work of Bernoulli years later after his death\n",
    "\n",
    "Euler worked out formula: y= e^x\n",
    "#  n = facorial_num (n!), n increases ...\n",
    "e = 1 + (1/n) + (1/n) + ...\n",
    "\n",
    "EULER formula = e^i*pi + 1 = 0\n",
    "\n",
    "e = 2.71828182845904523536028"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " [1.0, 0.5, 0.16666666666666666, 0.041666666666666664, 0.008333333333333333, 0.001388888888888889, 0.0001984126984126984, 2.48015873015873e-05, 2.7557319223985893e-06, 2.755731922398589e-07, 2.505210838544172e-08, 2.08767569878681e-09, 1.6059043836821613e-10, 1.1470745597729725e-11, 7.647163731819816e-13, 4.779477332387385e-14, 2.8114572543455206e-15, 1.5619206968586225e-16, 8.22063524662433e-18, 4.110317623312165e-19, 1.9572941063391263e-20, 8.896791392450574e-22, 3.868170170630684e-23, 1.6117375710961184e-24]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x_ = np.arange(1,25)\n",
    "e1 = []\n",
    "for i in x_:\n",
    "    k = (1/math.factorial(i))\n",
    "    e1.append(k)\n",
    "#     print(k) \n",
    "    \n",
    "    \n",
    "print('\\n',e1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.71828182845904523536028\n",
    "# type(x_)\n",
    "type(e1)\n",
    "# e[3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "6\n",
      "24\n",
      "120\n",
      "720\n",
      "5040\n",
      "40320\n",
      "362880\n",
      "3628800\n",
      "39916800\n",
      "479001600\n",
      "6227020800\n",
      "87178291200\n",
      "1307674368000\n",
      "20922789888000\n",
      "355687428096000\n",
      "6402373705728000\n",
      "121645100408832000\n",
      "2432902008176640000\n",
      "51090942171709440000\n",
      "1124000727777607680000\n",
      "25852016738884976640000\n",
      "620448401733239439360000\n"
     ]
    }
   ],
   "source": [
    "y_ = np.arange(1,25)\n",
    "e2 = []\n",
    "for i in y_:\n",
    "    y = math.factorial(i)\n",
    "    e2.append(y)\n",
    "    print(y) \n",
    "# print(e2)\n",
    "\n",
    "\n",
    "# for i in n:\n",
    "#     k = (1/math.factorial(i))\n",
    "#     e.append(k)\n",
    "#     print(k) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type(y_)\n",
    "type(e2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcc9e9b490>]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = x_\n",
    "y = y_\n",
    "plt.plot(x,y)\n",
    "# https://en.wikipedia.org/wiki/Factorial\n",
    "#  the plot works fine and is exactly what Wiki has"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1! = [1]\n",
      "2! = [1, 2]\n",
      "3! = [1, 2, 6]\n",
      "4! = [1, 2, 6, 24]\n",
      "5! = [1, 2, 6, 24, 120]\n",
      "6! = [1, 2, 6, 24, 120, 720]\n",
      "7! = [1, 2, 6, 24, 120, 720, 5040]\n",
      "8! = [1, 2, 6, 24, 120, 720, 5040, 40320]\n",
      "9! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880]\n",
      "10! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800]\n",
      "11! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800]\n",
      "12! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600]\n",
      "13! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800]\n",
      "14! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200]\n",
      "15! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000]\n",
      "16! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000]\n",
      "17! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000, 355687428096000]\n",
      "18! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000, 355687428096000, 6402373705728000]\n",
      "19! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000, 355687428096000, 6402373705728000, 121645100408832000]\n",
      "20! = [1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000, 355687428096000, 6402373705728000, 121645100408832000, 2432902008176640000]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import math\n",
    "\n",
    "e = []\n",
    "for i in range(1,21):\n",
    "    x = math.factorial(i)\n",
    "    e.append(x)\n",
    "    print('{}! = {}'.format(i, e))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2432902008176640000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "e[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Million</td>\n",
       "      <td>1e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Billion</td>\n",
       "      <td>1e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trillion</td>\n",
       "      <td>1e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quadrillion</td>\n",
       "      <td>1e+16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quintillion</td>\n",
       "      <td>1e+19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Sescentillion</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Septingentillion</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Octingentillion</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Nongentillion</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Millinillion</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Scale\n",
       "0            Million  1e+07\n",
       "1            Billion  1e+10\n",
       "2           Trillion  1e+13\n",
       "3        Quadrillion  1e+16\n",
       "4        Quintillion  1e+19\n",
       "..               ...    ...\n",
       "63     Sescentillion    inf\n",
       "64  Septingentillion    inf\n",
       "65   Octingentillion    inf\n",
       "66     Nongentillion    inf\n",
       "67      Millinillion    inf\n",
       "\n",
       "[68 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "df = pd.read_html('https://en.wikipedia.org/wiki/Names_of_large_numbers#Standard_dictionary_numbers')\n",
    "df[0:2]\n",
    "\n",
    "numbers = pd.DataFrame(\n",
    "    {'Name': ['Million','Billion','Trillion',\n",
    "              'Quadrillion','Quintillion','Sextillion',\n",
    "              'Septillion','Octillion','Nonillion',\n",
    "              'Decillion','Undecillion','Duodecillion',\n",
    "              'Tredecillion','Quattuordecillion','Quindecillion',\n",
    "              'Sexdecillion','Septendecillion','Octodecillion',\n",
    "              'Novemdecillion','Vigintillion','Unvigintillion',\n",
    "              'Duovigintillion','Tresvigintillion','Quattuorvigintillion',\n",
    "              'Quinvigintillion','Sesvigintillion','Septemvigintillion',\n",
    "              'Octovigintillion','Novemvigintillion','Trigintillion',\n",
    "              'Untrigintillion','Duotrigintillion','{Googol}',\n",
    "              'Trestrigintillion','Quattuortrigintillion','Quintrigintillion',\n",
    "              'Sestrigintillion','Septentrigintillion','Octotrigintillion',\n",
    "              'Noventrigintillion','Quadragintillion','Quinquagintillion',\n",
    "              'Sexagintillion','Septuagintillion','Octogintillion',\n",
    "              'Nonagintillion','Centillion','Uncentillion',\n",
    "              'Decicentillion','Undecicentillion','Viginticentillion',\n",
    "              'Unviginticentillion','Trigintacentillion','Quadragintacentillion',\n",
    "              'Quinquagintacentillion','Sexagintacentillion','Septuagintacentillion',\n",
    "              'Octogintacentillion','Nonagintacentillion','Ducentillion',\n",
    "              'Trecentillion','Quadringentillion','Quingentillion',\n",
    "              'Sescentillion','Septingentillion','Octingentillion',\n",
    "              'Nongentillion','Millinillion'\n",
    "             ],\n",
    "     'Scale': [10e6, 10e9,10e12,10e15,10e18,10e21,10e24,10e27,\n",
    "               10e30, 10e33, 10e36,10e39,10e42,10e45,10e48,\n",
    "               10e51,10e54,10e57,10e60,10e63,10e66,10e69,10e72,\n",
    "               10e75,10e78,10e81,10e84,10e87,10e90,10e93,10e96,\n",
    "               10e99,'10e100',10e102,10e105,10e108,10e111,10e114,\n",
    "               10e117,10e120,10e123,10e153,10e183,10e213,10e243,\n",
    "               10e273,10e303,10e306,10e333,10e336,10e363,10e366,\n",
    "               10e393,10e423,10e453,10e483,10e513,10e543,10e573,\n",
    "               10e603,10e903,10e1203,10e1503,10e1803,10e2103,\n",
    "               10e2403,10e2703,10e3003\n",
    "              ]\n",
    "    },\n",
    "    columns=['Name','Scale']\n",
    ")\n",
    "# 'Centillion'\n",
    "# https://en.wikipedia.org/wiki/Order_of_magnitude\n",
    "\n",
    "numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# https://en.wikipedia.org/wiki/Order_of_magnitude#Uses\n",
    "magnitude = pd.read_html('https://en.wikipedia.org/wiki/Order_of_magnitude#Uses')\n",
    "magnitude\n",
    "\n",
    "#https://www.machinelearningplus.com/data-manipulation/101-python-datatable-exercises-pydatatable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prefix (Symbol)                     Decimal  Powerof ten  \n",
    " 0                        yocto- (y)  0.000000000000000000000001        10−24   \n",
    " 1                        zepto- (z)     0.000000000000000000001        10−21   \n",
    " 2                         atto- (a)        0.000000000000000001        10−18   \n",
    " 3                        femto- (f)           0.000000000000001        10−15   \n",
    " 4                         pico- (p)              0.000000000001        10−12   \n",
    " 5                         nano- (n)                 0.000000001         10−9   \n",
    " 6   micro- (µ)(lowercase Mu symbol)                    0.000001         10−6   \n",
    " 7                        milli- (m)                       0.001         10−3   \n",
    " 8                        centi- (c)                        0.01         10−2   \n",
    " 9                         deci- (d)                         0.1         10−1   \n",
    " 10                              NaN                           1          100   \n",
    " 11             deca- (da)U+3372 (㍲)                          10          10\n",
    "  12                       hecto- (h)                         100          102   \n",
    " 13                        kilo- (k)                        1000          103   \n",
    " 14                        mega- (M)                     1000000          106   \n",
    " 15                        giga- (G)                  1000000000          109   \n",
    " 16                        tera- (T)               1000000000000         1012   \n",
    " 17                        peta- (P)            1000000000000000         1015   \n",
    " 18                         exa- (E)         1000000000000000000         1018   \n",
    " 19                       zetta- (Z)      1000000000000000000000         1021   \n",
    " 20                       yotta- (Y)   1000000000000000000000000         1024 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3) (1, 3, 2) (2, 1, 3) (2, 3, 1) (3, 1, 2) (3, 2, 1)\n"
     ]
    }
   ],
   "source": [
    "# 20_922_789_888_000\n",
    "\n",
    "# PERMUTATIONS\n",
    "from itertools import permutations \n",
    "l = list(permutations(range(1, 4))) \n",
    "print(*l)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('A', 'C', 'G', 'T'), ('A', 'C', 'T', 'G'), ('A', 'G', 'C', 'T'), ('A', 'G', 'T', 'C'), ('A', 'T', 'C', 'G'), ('A', 'T', 'G', 'C'), ('C', 'A', 'G', 'T'), ('C', 'A', 'T', 'G'), ('C', 'G', 'A', 'T'), ('C', 'G', 'T', 'A'), ('C', 'T', 'A', 'G'), ('C', 'T', 'G', 'A'), ('G', 'A', 'C', 'T'), ('G', 'A', 'T', 'C'), ('G', 'C', 'A', 'T'), ('G', 'C', 'T', 'A'), ('G', 'T', 'A', 'C'), ('G', 'T', 'C', 'A'), ('T', 'A', 'C', 'G'), ('T', 'A', 'G', 'C'), ('T', 'C', 'A', 'G'), ('T', 'C', 'G', 'A'), ('T', 'G', 'A', 'C'), ('T', 'G', 'C', 'A')]\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "# 'adenine', 'cytosine', 'guanine','thymine'\n",
    "#  1,2,3,4\n",
    "gene_bases = 'A', 'C', 'G','T'\n",
    "p = list(permutations(gene_bases))\n",
    "print(p)\n",
    "print(len(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A C G T\n",
      "A C T G\n",
      "A G C T\n",
      "A G T C\n",
      "A T C G\n",
      "A T G C\n",
      "C A G T\n",
      "C A T G\n",
      "C G A T\n",
      "C G T A\n",
      "C T A G\n",
      "C T G A\n",
      "G A C T\n",
      "G A T C\n",
      "G C A T\n",
      "G C T A\n",
      "G T A C\n",
      "G T C A\n",
      "T A C G\n",
      "T A G C\n",
      "T C A G\n",
      "T C G A\n",
      "T G A C\n",
      "T G C A\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    " \n",
    "# st = \"ABC\"\n",
    "gene_bases = ['A', 'C', 'G','T']\n",
    "per = itertools.permutations(gene_bases)\n",
    " \n",
    "for val in per:\n",
    "    print(*val) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "coin_flips = 'H', 'T'\n",
    "per = itertools.permutations(coin_flips)\n",
    "flips =[]\n",
    "for val in per:\n",
    "    flips.append(f)\n",
    "#     print(val)\n",
    "    \n",
    "print(len(flips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 coin flips = 1048576 permutations\n"
     ]
    }
   ],
   "source": [
    "n= 2\n",
    "flips = 20\n",
    "coin_flips = pow(n,flips)\n",
    "print(\"{} coin flips = {} permutations\".format(flips,coin_flips))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.plot(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A C\n",
      "A G\n",
      "A T\n",
      "C G\n",
      "C T\n",
      "G T\n"
     ]
    }
   ],
   "source": [
    "#  COMBINATIONS\n",
    "import itertools\n",
    " \n",
    "# st = \"ABCDE\"\n",
    "gene_bases = ['A', 'C', 'G','T']\n",
    "com = itertools.combinations(gene_bases, 2)\n",
    " \n",
    "for val in com:\n",
    "    print(*val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('H', 'T')\n",
      "('H', 'H')\n",
      "('H', 'T')\n",
      "('T', 'H')\n",
      "('T', 'T')\n",
      "('H', 'T')\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    " \n",
    "# values = [1, 2, 3, 4]\n",
    "values = 'H','T','H','T'\n",
    "# values = np.arange(5)\n",
    "com = itertools.combinations(values, 2)\n",
    " \n",
    "for val in com:\n",
    "    print(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ebd98edf37c882abfc3c2c2e22e82f39cf4b7404a946780827c22fb95e639f1c"
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  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit ('base': conda)",
   "name": "python3"
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