MyNote_SciPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b965c230",
   "metadata": {},
   "source": [
    "# <font color=blue>SciPy</font>\n",
    "\n",
    "### <font color=green>Toolboxes for Computations optimization,image processing, statistics, signal processing</font>\n",
    "### <font color=green>Operates on numpy arrays</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "014435cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ----- Generating random Vars in Python ----- #\n",
    "import scipy.stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(0) # For reproducibility\n",
    "\n",
    "# Using NumPy\n",
    "\n",
    "samples = np.random.normal(size=1000)\n",
    "num_bins = 50\n",
    "plt.hist(samples,50,color='blue')\n",
    "plt.title('Univariate Normal Dist using NumPy')\n",
    "plt.show()\n",
    "\n",
    "# Using SciPy\n",
    "\n",
    "samples = scipy.stats.norm.rvs(size=1000)\n",
    "num_bins = 50\n",
    "plt.hist(samples,50,color='blue')\n",
    "plt.title('Univariate Normal Dist using SciPy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b353fc5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}