{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "f = plt.figure()\nplt.hist(train_bookings['date_time'].values, bins=100, alpha=0.5, normed=True, label='train bookings')\nplt.hist(test_bookings['date_time'].values, bins=50, alpha=0.5, normed=True, label='test bookings')\nplt.hist(train_clicks['date_time'].values, bins=100, alpha=0.5, normed=True, label='train clicks')\nplt.title('Search time distribution')\nplt.legend(loc='best')\nf.savefig('SearchTime.png', dpi=300)\nplt.show()\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "f = plt.figure()\nplt.hist(train_bookings['srch_ci'].values, bins=100, alpha=0.5, normed=True, label='train bookings')\nplt.hist(test_bookings['srch_ci'].dropna().values, bins=50, alpha=0.5, normed=True, label='test bookings')\nplt.hist(train_clicks['srch_ci'].dropna().values, bins=100, alpha=0.5, normed=True, label='train clicks')\nplt.title('Checkin time')\nplt.legend(loc='best')\nf.savefig('CheckinTime.png', dpi=300)\nplt.show()\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages\n# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats\n# For example, here's several helpful packages to load in \n\nlibrary(ggplot2) # Data visualization\nlibrary(readr) # CSV file I/O, e.g. the read_csv function\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nsystem(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output."
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This Julia environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/julia docker image: https://github.com/kaggle/docker-julia\n# For example, here's a helpful package to load in \n\nusing DataFrames # data processing, CSV file I/O - e.g. readtable(\"../input/MyTable.csv\")\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nrun(`ls ../input`)\n\n# Any results you write to the current directory are saved as output."
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}