DataScience365/Day-14/Probability Distributions part-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Probability Distributions`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "# for inline plots in jupyter\n",
    "import matplotlib.pyplot as plt# import matplotlib\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "styles = ['seaborn-notebook', 'seaborn', 'seaborn-darkgrid', 'classic', \n",
    "          '_classic_test', 'seaborn-poster', 'tableau-colorblind10', 'grayscale', \n",
    "          'fivethirtyeight', 'seaborn-ticks', 'seaborn-dark', \n",
    "          'dark_background', 'seaborn-pastel', \n",
    "          'fast', 'Solarize_Light2', 'seaborn-colorblind', 'seaborn-white',\n",
    "          'seaborn-dark-palette',\n",
    "          'bmh', 'seaborn-talk', 'seaborn-paper', 'seaborn-deep', 'seaborn-bright', \n",
    "          'seaborn-muted', \n",
    "          'seaborn-whitegrid', 'ggplot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `5. Poisson Distribution`\n",
    "\n",
    "1. The Poisson distribution is used to model the number of events occurring within a given time interval.\n",
    "\n",
    "![](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQsTiqq3hOFW7FErL-WEXm1TrZuUzcTOli84FmvIVjwcFml4bac)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import poisson module from scipy.stats to generate poisson random variables.\n",
    "from scipy.stats import poisson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Frequency'), Text(0.5,0,'Poisson')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MOKrqiSSvB24AVgBbququMbclSYekqQgOgKq6Hrh+iT5uKJu8JpTfbXot5+/nd5siU7FzXJI0OaZlH4ckaUIYHH2W82VNkpyQ5B+S3J3kriRvHHdPw5ZkRZLPJ/nbcfcyTEmOTHJ1ki92/34/Mu6ehinJr3e/k3cm+UiS54y7p4OVZEuSvUnu7KsdnWR7knu6x6PG2eMwGBydvsuanAOcBLw6yUnj7WqongB+s6peCJwGXLzMvh/AG4G7x93ECPwp8HdV9f3Ai1lG3zHJGuDXgPVV9SJ6B7+8arxdPSNXAGfvV9sE3FhVa4Ebu/WpZnA8ZVlf1qSq9lTV57rlr9D7z2fNeLsaniTHA+cB7xt3L8OU5LuBnwDeD1BVj1fVf4y3q6FbCRyWZCVwOPudozVNquom4KH9yucDW7vlrcArlrSpETA4njLfZU2WzX+s/ZLMACcDnx1vJ0P1TuC3gf8adyND9nxgH/CBbjPc+5IcMe6mhqWqHgD+GLgP2AM8UlV/P96uhu7YqtoDvT/ggGPG3M8zZnA8JfPUlt0hZ0m+E7gGuKSqHh13P8OQ5GeBvVV167h7GYGVwCnAZVV1MvA1lsGmjjnd9v7zgecB3wcckeT/jrcrLcbgeMqilzWZdkmeTS80PlRVHx93P0N0OvDyJLvobWJ8aZK/Gm9LQzMLzFbV3OzwanpBslz8NPDlqtpXVd8EPg786Jh7GrYHkxwH0D3uHXM/z5jB8ZRlfVmTJKG3nfzuqnr7uPsZpqp6c1UdX1Uz9P7d/l9VLYu/WqvqX4H7k7ygK50JLKf70NwHnJbk8O539EyW0c7/zjZgQ7e8AfjEGHsZiqk5c3zUDoHLmpwOvAa4I8ltXe0t3Rn5mmxvAD7U/UFzL3DRmPsZmqr6bJKrgc/RO/Lv80zxmdZJPgKcAaxKMgtcCmwGrkryWnpBecH4OhwOzxyXJDVxU5UkqYnBIUlqYnBIkpoYHJKkJgaHJKmJwSENKMmTSW7rruL6sSSHLzL+n5aqN2kpGRzS4L5eVeu6q7g+DvzKQoOrarmdAS0BBod0sP4/8D8BkvxGNwu5M8klcwOSfLV7PC7JTX2zlR/v7h1yRbd+R5Jf78auS/KZJLcnuXbu3g1JPpXkj5LcnOSfk/z4GL6zBBgcUrPu8t/n0DsL/4foncn9Enr3OXldkpP3e8kvAjdU1Tp699O4DVgHrKmqF1XVDwAf6MZeCbypqn4QuIPemcdzVlbVqcAl+9WlJWVwSIM7rLtcyw56l454P/BjwLVV9bWq+iq9i/TtPxu4BbgoyVuBH+juh3Iv8Pwkf5bkbODRJN8DHFlVn+5et5XevTjmzF2Y8lZgZujfThqQwSENbm4fx7qqekN3w6/5Lsf/bbqb+/wE8ADwwSQXVtXD9GYfnwIuZrAbUH2je3wSrzOnMTI4pGfmJuAV3dVdjwBeSW//x7ckeS69+4W8l94s5ZQkq4BnVdU1wO8Cp1TVI8DDffsvXgN8GmnC+FeL9AxU1eeSXAHc3JXeV1Wf32/YGcBvJfkm8FXgQnp3l/xAkrk/3t7cPW4A3tMd6rusroSr5cOr40qSmripSpLUxOCQJDUxOCRJTQwOSVITg0OS1MTgkCQ1MTgkSU0MDklSk/8GJor5/D1/xEMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Poisson random variable is typically used to model the number of times an event \n",
    "# happened in a time interval. For example, number of users visited your website in an interval \n",
    "# can be thought of a Poisson process. Poisson distribution is described in terms of the rate (mu) at which\n",
    "# the events happen. We can generate Poisson random variables in Python using poisson.rvs.\n",
    "poisson = poisson.rvs(mu=3, size=10000)\n",
    "# Let us generate 10000 random numbers from Poisson random variable with mu = 0.3 and plot them.\n",
    "ax = sns.distplot(poisson,\n",
    "                  kde=False,\n",
    "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "ax.set(xlabel='Poisson', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PoissonDistribution(palette=\"dark\",kde = False,style = \"ggplot\"):\n",
    "    plt.figure(figsize=(13,10))\n",
    "    plt.style.use(style)\n",
    "    sns.set_palette(palette)\n",
    "    ax = sns.distplot(poisson,\n",
    "                  kde=kde,\n",
    "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "    ax.set(xlabel='Poisson', ylabel='Frequency')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4060fbc05ce48dd8ab17513183d812f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='palette', index=4, options=('deep', 'muted', 'pastel', 'bright', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(PoissonDistribution,palette = [\"deep\", \"muted\", \"pastel\", \"bright\", \n",
    "                    \"dark\", \"colorblind\",\"Set3\",\"Set2\"],kde = [True,False],style = styles);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `6. Beta Distribution`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We can understand Beta distribution as a distribution for probabilities. Beta distribution is a continuous distribution taking values from 0 to 1. It is defined by two parameters alpha and beta, depending on the values of alpha and beta they can assume very different distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Frequency'), Text(0.5,0,'Beta(1,1)')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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79+46ePBg/Sq7RlxcnOLi4jzb7d+/v44cOaLo6GhVVFRIkioqKtS6desG7wMA0HiWw6Jr165at26dqqurJV05dfa9995Tly5dGrzzNm3aKC4uTiUlJZKu/MBSx44dlZKSovz8fElSfn6++vbt2+B9AAAaz2YYhmFlwbKyMi1cuFCHDx9WVFSUqqqq1K1bNz3//PNq27Ztgwv46quvtHTpUtXU1Kht27ZKTU2VYRjKzMxUeXm5HA6Hpk+fbunU2auhU18d+i1v0HoAEAwuHZ7t82Eoy2FxVXl5uSoqKhQTExN0B5QJCwChyB9hUa97dVRWVqq4uFjFxcVyOBxyuVw6ffp0gwoEADQdlsOiuLhY06ZN07Zt27RhwwZJ0smTJ7VixQqfFQcACA6Ww2L16tWaNm2a5syZo/DwcElXzoY6fPiwz4oDAAQHy2Fx6tQp9erVq848u92u2tparxcFAAgulsOiY8eO2rNnT515e/fu1W233eb1ogAAwcXyFdzjxo3T7373O/Xp00fV1dVavny5du7cqRkzZviyPgBAELAcFsnJyVqwYIG2bdumFi1ayOFwaP78+YqLi/NlfQCAIGApLNxut37zm99ozpw5evTRR31dEwAgyFg6ZhEWFqaysjLV8/o9AMBNwvIB7p/85CdasWKFTp06JbfbXecfAODmZvmYxbJlyyRJW7du/c5j69ev915FAICgYxoWZ86cUZs2bbR48WJ/1AMACEKmw1BTp06VJMXHxys+Pl5r1qzx/P/qPwDAzc00LL59UHv//v0+KwYAEJxMw8Jms/mjDgBAEDM9ZlFbW6t9+/Z5pt1ud51pSbr99tu9XxkAIGiYhkV0dLSWLFnimY6KiqozbbPZOPgNADc507DIzs72Rx0AgCBWr1/KAwCEJsICAGCKsAAAmCIsAACmCAsAgCnLNxL0JbfbrVmzZik2NlazZs1SWVmZsrKyVFVVpa5du2rKlCmy24OiVAAISUHRs/j000/VoUMHz/Qf/vAHjRgxQgsXLtQtt9yivLy8AFYHAAh4WJw+fVq7du3SsGHDJF25F9X+/fvVv39/SdKQIUNUWFgYyBIBIOQFPCxWr16tsWPHeu5BVVlZqcjISIWHh0uSYmNj5XK5AlkiAIS8gB4I2Llzp6Kjo5WYmNigu9nm5uYqNzdXkpSWliaHw+HtEgEg6Nntdp9//gU0LA4cOKCioiLt3r1b1dXV+uabb7R69WpduHBBtbW1Cg8Pl8vlUmxs7HXXdzqdcjqdnuny8nJ/lQ4AQaOmpqbBn38JCQmWlgtoWDzxxBN64oknJF35nYw///nPev7555WRkaEdO3ZowIAB2rJli1JSUgJZJgCEvIAfs7ien//85/r44481ZcoUVVVVaejQoYEuCQBCms349k/hNWElJSUNWq9Dv+VergQA/OfS4dk+H4YKyp4FACC4EBYAAFOEBQDAFGEBADBFWAAATBEWAABThAUAwBRhAQAwRVgAAEwRFgAAU4QFAMAUYQEAMEVYAABMERYAAFOEBQDAFGEBADBFWAAATBEWAABThAUAwBRhAQAwRVgAAEwRFgAAU4QFAMAUYQEAMGUP5M7Ly8uVnZ2tM2fOyGazyel0avjw4aqqqlJmZqZOnTql+Ph4vfDCC4qKigpkqQAQ0gIaFuHh4Ro3bpwSExP1zTffaNasWbrjjju0ZcsW9erVS6NGjdKmTZu0adMmjR07NpClAkBIC+gwVExMjBITEyVJLVu2VIcOHeRyuVRYWKjBgwdLkgYPHqzCwsJAlgkAIS9ojlmUlZXpyJEj6t69u86ePauYmBhJVwLl3LlzAa4OAEJbQIehrrp48aLS09M1YcIERUZGWl4vNzdXubm5kqS0tDQ5HA5flQgAQctut/v88y/gYVFTU6P09HQNHDhQ/fr1kyRFR0eroqJCMTExqqioUOvWra+7rtPplNPp9EyXl5f7pWYACCY1NTUN/vxLSEiwtFxAh6EMw9DSpUvVoUMHPfLII575KSkpys/PlyTl5+erb9++gSoRAKAA9ywOHDigrVu36rbbbtOMGTMkSY8//rhGjRqlzMxM5eXlyeFwaPr06YEsEwBCns0wDCPQRXhLSUlJg9br0G+5lysBAP+5dHj2zT0MBQBoGggLAIApwgIAYIqwAACYIiwAAKYICwCAKcICAGCKsAAAmCIsAACmCAsAgCnCAgBgirAAAJgiLAAApggLAIApwgIAYIqwAACYIiwAAKYICwCAKcICAGCKsAAAmCIsAACmCAsAgCnCAgBgirAAAJiyB7qA77Nnzx6tWrVKbrdbw4YN06hRowJdEgCEpKDtWbjdbq1cuVKzZ89WZmamtm/fruPHjwe6LAAISUEbFocOHVL79u3Vrl072e123XfffSosLAx0WQAQkoI2LFwul+Li4jzTcXFxcrlcAawIAEJX0B6zMAzjO/NsNlud6dzcXOXm5kqS0tLSlJCQ0LB9HZvXoPUAIFg09PPPqqDtWcTFxen06dOe6dOnTysmJqbOMk6nU2lpaUpLS2vUvmbNmtWo9Zsi2hwaaHNo8EebgzYsunXrptLSUpWVlammpkYFBQVKSUkJdFkAEJKCdhgqPDxcEydO1Ouvvy632637779fnTp1CnRZABCSgjYsJOmuu+7SXXfd5fP9OJ1On+8j2NDm0ECbQ4M/2mwzrnckGQCAawTtMQsAQPAI6mEobzO7fcjly5e1ePFiffnll2rVqpWmTZumtm3bBqha7zBr88cff6zNmzcrPDxcrVu31qRJkxQfHx+gar3D6m1iduzYoYyMDL3xxhvq1q2bn6v0LittLigo0J/+9CfZbDZ17txZU6dODUCl3mPW5vLycmVnZ+v8+fNyu9164okn/DKs7Ss5OTnatWuXoqOjlZ6e/p3HDcPQqlWrtHv3bjVv3lypqalKTEz0XgFGiKitrTUmT55snDx50rh8+bLx0ksvGceOHauzzF//+ldj2bJlhmEYxj//+U8jIyMjEKV6jZU2792717h48aJhGIbx2WefhUSbDcMwLly4YMydO9eYPXu2cejQoQBU6j1W2lxSUmLMmDHDqKysNAzDMM6cOROIUr3GSpuXLl1qfPbZZ4ZhGMaxY8eM1NTUQJTqNfv37zcOHz5sTJ8+/bqP79y503j99dcNt9ttHDhwwHjllVe8uv+QGYaycvuQoqIiDRkyRJLUv39/7du377oXBzYVVtp8++23q3nz5pKkpKSkJn+VvNXbxKxfv14jR45Us2bNAlCld1lp8+bNm/XQQw8pKipKkhQdHR2IUr3GSpttNpsuXLggSbpw4cJ3rtNqanr06OF5/a6nqKhIgwYNks1mU3Jyss6fP6+Kigqv7T9kwsLK7UOuXSY8PFyRkZGqrKz0a53eVN9bpuTl5al3797+KM1nrLT5yJEjKi8v19133+3v8nzCSptLSkpUWlqqX/3qV5ozZ4727Nnj7zK9ykqbf/rTn2rbtm169tln9cYbb2jixIn+LtOvXC6XHA6HZ9rbt0gKmbC4Xg/h27cPsbJMU1Kf9mzdulVffvmlRo4c6euyfMqszW63W2vWrNEvfvELf5blU1ZeZ7fbrdLSUr366quaOnWqli5dqvPnz/urRK+z0ubt27dryJAhWrp0qV555RUtWrRIbrfbXyX6na8/v0ImLKzcPuTaZWpra3XhwoXv7fYFOyttlqTPP/9cGzdu1Msvv9zkh2XM2nzx4kUdO3ZMv/71r/Xcc8/piy++0O9//3sdPnw4EOV6hZXXOTY2Vn379pXdblfbtm2VkJCg0tJSf5fqNVbanJeXp3vvvVeSlJycrMuXLzfpkQIzcXFxKi8v90zf6P3eUCETFlZuH3L33Xdry5Ytkq6cKdOzZ88m3bOw0uYjR45oxYoVevnll5v8OLZk3ubIyEitXLlS2dnZys7OVlJSkl5++eUmfTaUldf5nnvu0b59+yRJ586dU2lpqdq1axeIcr3CSpsdDoenzcePH9fly5fVunXrQJTrFykpKdq6dasMw9DBgwcVGRnp1bAIqYvydu3apTVr1nhuHzJ69GitX79e3bp1U0pKiqqrq7V48WIdOXJEUVFRmjZtWpN+Q0nmbX7ttdf09ddfq02bNpKuvMFmzpwZ4Kobx6zN15o3b57GjRvXpMNCMm+zYRh69913tWfPHoWFhWn06NEaMGBAoMtuFLM2Hz9+XMuWLdPFixclSWPHjtWdd94Z4KobLisrS8XFxaqsrFR0dLTGjBmjmpoaSdKDDz4owzC0cuVK/ec//1FERIRSU1O9+ncdUmEBAGiYkBmGAgA0HGEBADBFWAAATBEWAABThAUAwBRhAfhJVlaW/v3vf3t9u0VFRcrKyvL6doFrhdQtyoFrPffcczpz5ozCwsJkt9uVnJysX/7yl3Xur3M9+/fv16JFi7R06VLL+zp69KiOHj3quS14RUWFli9fri+//FIVFRVavHjx994O/7333lNhYaFOnDih0aNHa8yYMZ7HUlJStG7dOh09elSdO3e2XBNQH/QsENJmzpyptWvXatmyZYqOjtY777zjk/38/e9/18CBAz13BLDZbOrdu7defPFFS+u3b99eY8eOveHvMQwYMEC5ubleqxf4NnoWgKSIiAj1799fa9askXTlh7DWrVunf/3rX6qpqVHfvn01YcIEud1uzZ8/XzU1NRo3bpwk6a233pLL5dKqVat04sQJRUREqF+/fho/frzs9itvsT179mjy5Mme/bVp00YPPfSQamtrLdV39db527Ztu+7jPXr00KJFi/TUU0819CkAvhc9C0DSpUuXVFBQoKSkJEnSH//4R5WWlmrBggVauHChXC6XPvjgA7Vo0UKzZ89WTEyM1q5dq7Vr1yo2NlZhYWEaP368Vq5cqd/+9rfat2+fPvvsM0lXbl5YVlamhIQEn9XfsWNHnTp1yvP7DYC3ERYIaQsWLNCECRM0fvx47d27VyNHjpRhGNq8ebPGjx+vqKgotWzZUqNHj9b27dtvuJ3ExEQlJycrPDxcbdu2ldPpVHFxsSR5PsBbtGjhs3Zc3TZhAV9hGAohbcaMGbrjjjvkdrtVWFioV199VQsWLNClS5c0a9Ysz3KGYXzvbyGUlJTo3Xff1eHDh1VdXa3a2lrP7x9HRkZKutLDiIiI8Ek7rt4s7+q+AG8jLABJYWFh6tevn5YvX66DBw8qIiJCGRkZio2N/c6y17tt/dtvv60uXbpo6tSpatmypT755BPt2LFD0pVv/e3atVNJSYnPbpF9/PhxxcfHExbwGYahAF3pORQWFur8+fPq1KmThg0bptWrV+vs2bOSrvxk5dWfIo2OjlZlZWWdIZ9vvvlGkZGRatGihU6cOKG//e1vdbbfp08fz7DUVdXV1bp8+bIkqaamRtXV1Z7H3n//fc2bN88zffXxqz2c6urqOj2d4uJi9enTxztPBnAd3KIcIeva6yxsNpvi4+M1atQoDRw4UNXV1frggw9UUFCgyspKxcbG6oEHHtDw4cMlSTk5OSoqKpLb7VZGRoZOnjyp5cuX6/Tp0+ratat69uypffv26bXXXpMkff3118rKylJ6erqnZ3LttRJXvf/++5KkJUuWqE2bNnr88cclSdnZ2crPz6+zbGpqqucsqRdffFFTpkxRly5dfPFUAYQF4C9vvfWW7r33Xt1zzz2my86YMUNz585Vq1atTJctKirS1q1bNX36dG+UCVwXYQEAMMUxCwCAKcICAGCKsAAAmCIsAACmCAsAgCnCAgBgirAAAJgiLAAApv4//guG0rWskYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us generate 10000, random numbers from Beta distribution \n",
    "# with alpha = 1 and beta = 1. The histogram of Beta(1,1) is a uniform distribution.\n",
    "from scipy.stats import beta\n",
    "beta = beta.rvs(1, 1, size=10000)\n",
    "ax = sns.distplot(beta,\n",
    "                  kde=False,\n",
    "                  bins=100,\n",
    "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "ax.set(xlabel='Beta(1,1)', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(20.2,0.5,'Frequency'), Text(0.5,20.2,'Beta(10,1)')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us generate 10000, random numbers from Beta distribution with alpha = 10 and beta = 1. \n",
    "# The histogram of Beta(10,1) is skewed towards right.\n",
    "from scipy.stats import beta\n",
    "beta_right = beta.rvs(10, 1, size=10000)\n",
    "sns.distplot(beta_right,\n",
    "             kde=False,\n",
    "             bins=50,\n",
    "             hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "ax.set(xlabel='Beta(10,1)', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Frequency'), Text(0.5,0,'Beta(1,10)')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us generate 10000, random numbers from Beta distribution with alpha = 1 and beta = 10.\n",
    "# The histogram of Beta(1,10) is skewed towards left.\n",
    "beta_left = beta.rvs(1, 10, size=10000)\n",
    "ax = sns.distplot(beta_left,\n",
    "                kde=False,\n",
    "                bins=100,\n",
    "                hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "ax.set(xlabel='Beta(1,10)', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Frequency'), Text(0.5,0,'Beta(10,10)')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us generate 10000, random numbers from Beta distribution with alpha = 10 and beta = 10. \n",
    "# The histogram of Beta(10,10) is symmetric and looks like a normal distribution\n",
    "beta_symmetric = beta.rvs(10, 10, size=10000)\n",
    "ax = sns.distplot(beta_symmetric,\n",
    "                  kde=False,\n",
    "                  bins=100,\n",
    "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "ax.set(xlabel='Beta(10,10)', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BetaDistribution(palette=\"dark\",kde = False,style = \"ggplot\",kind = \"left\"):\n",
    "    plt.figure(figsize=(13,10))\n",
    "    plt.style.use(style)\n",
    "    sns.set_palette(palette)\n",
    "    if kind == \"left\":\n",
    "        ax = sns.distplot(beta_left,\n",
    "                kde=kde,\n",
    "                bins=100,\n",
    "                hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "        ax.set(xlabel='Beta(1,10)', ylabel='Frequency')\n",
    "    elif kind == \"right\":\n",
    "        ax = sns.distplot(beta_right,\n",
    "             kde=kde,\n",
    "             bins=50,\n",
    "             hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "        ax.set(xlabel='Beta(10,1)', ylabel='Frequency')\n",
    "    elif kind == \"symmetric\":\n",
    "        ax = sns.distplot(beta_symmetric,\n",
    "                  kde=kde,\n",
    "                  bins=100,\n",
    "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "        ax.set(xlabel='Beta(10,10)', ylabel='Frequency')       \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f6cdac369064aabaf06c8e7b4b03135",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='palette', index=4, options=('deep', 'muted', 'pastel', 'bright', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(BetaDistribution,palette = [\"deep\", \"muted\", \"pastel\", \"bright\", \n",
    "                    \"dark\", \"colorblind\",\"Set3\",\"Set2\"],kde = [True,False],style = styles,\n",
    "         kind = [\"left\",\"right\",\"symmetric\"]);"
   ]
  }
 ],
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