histogram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5e6f5c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5e69f1780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5e5e26940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "observations = sorted([round(random(), 2) * 10 for _ in range(20)])\n",
    "\n",
    "for intervals_number in [10, 5, 4]:\n",
    "    plt.hist(observations, intervals_number, density=True)\n",
    "    plt.show()"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}