CDF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "numbers = np.random.normal(0,1,100000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ecdf(data):\n",
    "    x_sorted = np.sort(data)\n",
    "    n = x_sorted.size\n",
    "    y = np.arange(1, n+1) / n\n",
    "    return x_sorted, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = get_ecdf(numbers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b2e57d5f9b0>"
      ]
     },
     "execution_count": 13,
     "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": [
    "plt.scatter(x=x, y=y)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}