rate_of_growth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ac87d07-d0fa-4cf4-86c5-299ad5947502",
   "metadata": {},
   "source": [
    "# Rate of Growth and Vision vs No Vision\n",
    "\n",
    "Book: P12 of The Art of Doing Science and Engineering by Richard W. Hamming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "052a61a7-40a7-47de-8d22-e493f081acfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f072c648-59a5-499c-88a3-a0d854793545",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vision_vs_no_vision(visions):\n",
    "    data_set = []\n",
    "    for vision in visions:\n",
    "    \n",
    "        no_vision = np.sqrt(vision)\n",
    "        data = [vision, no_vision]\n",
    "        # print(data)\n",
    "        data_set.append(data)\n",
    "        # print(data_set)\n",
    "        growth_vs_no_growth_data = pd.DataFrame(data_set, columns=[\"growth rate with vision\", \"growth rate with NO vision\"])\n",
    "        copy_of_data = growth_vs_no_growth_data.copy()\n",
    "        \n",
    "    return copy_of_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74efe225-b700-4365-b043-4841a878401d",
   "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>growth rate with vision</th>\n",
       "      <th>growth rate with NO vision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.732051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2.236068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2.449490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2.645751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2.828427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>3.162278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>3.316625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>3.464102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    growth rate with vision  growth rate with NO vision\n",
       "0                         0                    0.000000\n",
       "1                         1                    1.000000\n",
       "2                         2                    1.414214\n",
       "3                         3                    1.732051\n",
       "4                         4                    2.000000\n",
       "5                         5                    2.236068\n",
       "6                         6                    2.449490\n",
       "7                         7                    2.645751\n",
       "8                         8                    2.828427\n",
       "9                         9                    3.000000\n",
       "10                       10                    3.162278\n",
       "11                       11                    3.316625\n",
       "12                       12                    3.464102"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visions = [1, 2]\n",
    "visions = range(0, 13)\n",
    "vision_data = vision_vs_no_vision(visions)\n",
    "vision_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e77c173-5713-4995-80ea-21c305928e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd940dd2fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vision_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e432eef-d254-40ac-8c36-72334b5ed49f",
   "metadata": {},
   "source": [
    "---\n",
    "# Growth Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c68a2a0e-044d-44cd-8662-a4373c87bb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "MONTHS_IN_YEAR = 12\n",
    "def growth_rate(num_of_rates):\n",
    "    data_set = []\n",
    "    \n",
    "    for num_of_rate in num_of_rates:\n",
    "        rate = np.round(np.random.normal(0, 1), 2)\n",
    "        rate = np.abs(rate)\n",
    "        growth = round(np.exp(rate) * MONTHS_IN_YEAR, 2)\n",
    "        data = [rate, growth]\n",
    "        data_set.append(data)\n",
    "        growth_data = pd.DataFrame(data_set, columns=[\"growth factor\", \"growth amount\"])\n",
    "    \n",
    "    return growth_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db751f3d-38fd-423d-b9a4-71b35db03798",
   "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>growth factor</th>\n",
       "      <th>growth amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.24</td>\n",
       "      <td>15.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.08</td>\n",
       "      <td>35.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.19</td>\n",
       "      <td>14.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.77</td>\n",
       "      <td>25.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.25</td>\n",
       "      <td>15.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.68</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.05</td>\n",
       "      <td>34.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.45</td>\n",
       "      <td>18.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.76</td>\n",
       "      <td>69.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.93</td>\n",
       "      <td>82.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.50</td>\n",
       "      <td>19.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.84</td>\n",
       "      <td>27.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.41</td>\n",
       "      <td>18.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.13</td>\n",
       "      <td>37.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    growth factor  growth amount\n",
       "0            0.24          15.25\n",
       "1            1.08          35.34\n",
       "2            0.19          14.51\n",
       "3            0.77          25.92\n",
       "4            0.25          15.41\n",
       "5            0.68          23.69\n",
       "6            1.05          34.29\n",
       "7            0.45          18.82\n",
       "8            1.76          69.75\n",
       "9            1.93          82.67\n",
       "10           0.50          19.78\n",
       "11           0.84          27.80\n",
       "12           0.41          18.08\n",
       "13           1.13          37.15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rate = 0.1\n",
    "num_of_rates = range(1, 15)\n",
    "rate_of_growth = growth_rate(num_of_rates)\n",
    "rate_of_growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9078af6c-0bd0-42ee-bdb9-d4d648735e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd950041690>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rate_of_growth.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6335cf14-c62b-4317-9a97-97cbf8a38c03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}