{"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)\nimport csv\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\ndef read_data(filname, limit=None):\n    data = []\n    labels = []\n\n    csv_reader = csv.reader(open(filname, \"r\"), delimiter=\",\")\n    index = 0\n    for row in csv_reader:\n        index += 1\n        if index == 1:\n            continue\n\n        labels.append(int(row[0]))\n        row = row[1:]\n\n        data.append(np.array(np.int64(row)))\n\n        if limit != None and index == limit + 1:\n            break\n\n    return (data, labels)\n\nprint(\"Reading train data\")\ntrain, target = read_data(\"../input/train.csv\")\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": "print(train)"
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}