{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np_hist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-ee89c299b1bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_hist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np_hist' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np=[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
    "print(np_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-610cf18bf36c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'hist' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as x\n",
    "x=[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
    "hist.show(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'random'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-7ac5911bc016>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot Histogram on x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Frequency Histogram'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'random'"
     ]
    }
   ],
   "source": [
    "# Plot Histogram on x\n",
    "x = np.random.normal(size = 1000)\n",
    "plt.hist(x, bins=50)\n",
    "plt.gca().set(title='Frequency Histogram', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "hist() takes from 1 to 18 positional arguments but 100 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-18f2be4a62ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: hist() takes from 1 to 18 positional arguments but 100 were given"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "plt.hist(14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "commute_times = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
    "plt.hist(commute_times, 5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'fig'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-34d437455849>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mcommute_times\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.8\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m 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      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'fig'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "commute_times = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
    "plt.fig(commute_times, 10)\n",
    "commute_times.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
    "plt.ylabel(\"100\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "x=([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
    "plt.hist(x,5)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}