{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import 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\"))\ntrain = pd.read_csv(\"../input/train.csv\")\n\nprint(train.columns)\ndata = train.as_matrix()\nuniqueIds = np.unique(data[:,5])\nprint(\"Number of distinct places: \" + str(uniqueIds.shape[0]))\n#\nprint(data)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import matplotlib.pyplot as plt\ndataX = data[:1500,:]\nplt.figure()\nplt.scatter(dataX[:, 1], dataX[:, 2])\nplt.show()\nprint(dataX)"
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}