{"cells":[
 {
  "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."
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "category = pd.read_csv('../input/Category.csv')\nprint (category.info(), '\\n', category.shape, '\\n', category.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "Location = pd.read_csv('../input/Location.csv')\nprint (Location.info(), '\\n', Location.shape, '\\n', Location.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "ItemPairs_train = pd.read_csv('../input/ItemPairs_train.csv')\nprint(ItemPairs_train.info(), '\\n', ItemPairs_train.shape, '\\n', ItemPairs_train.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "ItemPairs_test = pd.read_csv('../input/ItemPairs_test.csv')\nprint(ItemPairs_test.info(), '\\n', ItemPairs_test.shape, '\\n', ItemPairs_test.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "ItemInfo_train = pd.read_csv('../input/ItemInfo_train.csv', encoding='utf-8')\nprint(ItemInfo_train.info(), '\\n', ItemInfo_train.shape, '\\n', ItemInfo_train.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "ItemInfo_test = pd.read_csv('../input/ItemInfo_test.csv')\nprint(ItemInfo_test.info(), '\\n', ItemInfo_test.shape, '\\n', ItemInfo_test.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "ItemInfo_test = pd.read_csv('../input/ItemInfo_test.csv')\nprint(ItemInfo_test.info(), '\\n', ItemInfo_test.shape, '\\n', ItemInfo_test.head())"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "Random_submission = pd.read_csv('../input/Random_submission.csv')\nprint(Random_submission.info(), '\\n', Random_submission.shape, '\\n', Random_submission.head())"
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}