{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from central_limit_theorem import *\n",
    "\n",
    "sample_sizes = [1, 2, 10, 100, 1000, 10000]\n",
    "number_of_samples = 10000\n",
    "\n",
    "for i, sample_size in enumerate(sample_sizes):\n",
    "    means = clt(sample_size=sample_size, number_of_samples=number_of_samples)\n",
    "#     print(means)\n",
    "    plt.subplot(len(sample_sizes), 1, i+1)\n",
    "    plt.hist(means, bins='auto')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from central_limit_theorem import *\n",
    "\n",
    "sample_sizes = [1000]\n",
    "number_of_samples = 10000\n",
    "\n",
    "for i, sample_size in enumerate(sample_sizes):\n",
    "    means = clt(sample_size=sample_size, number_of_samples=number_of_samples)\n",
    "#     print(means)\n",
    "    plt.subplot(len(sample_sizes), 1, i+1)\n",
    "    plt.hist(means, bins='auto')\n"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}