Code-Example-007-02-Normal-Distribution-with-Scipy-Plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade scipy matplotlib  statistics yellowbrick scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# from scipy.stats import norm\n",
    "from scipy import stats\n",
    "\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00154948 0.0015962  0.00164383 0.00169237 0.00174183 0.0017922\n",
      " 0.00184348 0.00189565 0.00194873 0.00200269 0.00205752 0.00211324\n",
      " 0.00216981 0.00222723 0.00228549 0.00234457 0.00240446 0.00246514\n",
      " 0.00252661 0.00258883 0.00265179 0.00271547 0.00277986 0.00284492\n",
      " 0.00291063 0.00297697 0.00304392 0.00311144 0.00317951 0.0032481\n",
      " 0.00331718 0.00338672 0.00345668 0.00352704 0.00359775 0.00366879\n",
      " 0.00374011 0.00381168 0.00388346 0.00395542 0.0040275  0.00409967\n",
      " 0.00417189 0.00424412 0.0043163  0.00438841 0.00446038 0.00453219\n",
      " 0.00460378 0.0046751  0.00474611 0.00481676 0.004887   0.00495679\n",
      " 0.00502607 0.0050948  0.00516293 0.00523041 0.00529718 0.00536321\n",
      " 0.00542845 0.00549283 0.00555632 0.00561887 0.00568043 0.00574094\n",
      " 0.00580037 0.00585866 0.00591578 0.00597167 0.00602628 0.00607959\n",
      " 0.00613153 0.00618207 0.00623117 0.00627878 0.00632487 0.00636939\n",
      " 0.00641231 0.0064536  0.00649321 0.00653112 0.00656729 0.00660168\n",
      " 0.00663428 0.00666505 0.00669396 0.00672099 0.00674611 0.00676931\n",
      " 0.00679056 0.00680985 0.00682715 0.00684245 0.00685574 0.006867\n",
      " 0.00687623 0.00688342 0.00688856 0.00689164 0.00689267 0.00689164\n",
      " 0.00688856 0.00688342 0.00687623 0.006867   0.00685574 0.00684245\n",
      " 0.00682715 0.00680985 0.00679056 0.00676931 0.00674611 0.00672099\n",
      " 0.00669396 0.00666505 0.00663428 0.00660168 0.00656729 0.00653112\n",
      " 0.00649321 0.0064536  0.00641231 0.00636939 0.00632487 0.00627878\n",
      " 0.00623117 0.00618207 0.00613153 0.00607959 0.00602628 0.00597167\n",
      " 0.00591578 0.00585866 0.00580037 0.00574094 0.00568043 0.00561887\n",
      " 0.00555632 0.00549283 0.00542845 0.00536321 0.00529718 0.00523041\n",
      " 0.00516293 0.0050948  0.00502607 0.00495679 0.004887   0.00481676\n",
      " 0.00474611 0.0046751  0.00460378 0.00453219 0.00446038 0.00438841\n",
      " 0.0043163  0.00424412 0.00417189 0.00409967 0.0040275  0.00395542\n",
      " 0.00388346 0.00381168 0.00374011 0.00366879 0.00359775 0.00352704\n",
      " 0.00345668 0.00338672 0.00331718 0.0032481  0.00317951 0.00311144\n",
      " 0.00304392 0.00297697 0.00291063 0.00284492 0.00277986 0.00271547\n",
      " 0.00265179 0.00258883 0.00252661 0.00246514 0.00240446 0.00234457\n",
      " 0.00228549 0.00222723 0.00216981 0.00211324 0.00205752 0.00200269\n",
      " 0.00194873 0.00189565 0.00184348 0.0017922  0.00174183 0.00169237\n",
      " 0.00164383 0.0015962 ]\n"
     ]
    }
   ],
   "source": [
    "# Plot between -10 and 10 with .001 steps.\n",
    "x_axis = np.arange(-100, 100, 1)\n",
    "\n",
    "# Calculating mean and standard deviation\n",
    "mean = statistics.mean(x_axis)\n",
    "sd = statistics.stdev(x_axis)\n",
    "\n",
    "# We use PDF function to sample from this normal distribution. \n",
    "n_array = stats.norm.pdf(x_axis, mean, sd)\n",
    "\n",
    "print(n_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(x_axis,n_array )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Another Example using Numpy Random Normal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 2000 samples\n",
    "# mean = 0 \n",
    "# st = 5 \n",
    "n_data = np.random.normal(0, 5, 2000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(n_data, 20, facecolor='g', alpha=0.50)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}