{"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 matplotlib.pyplot as plt\nimport os\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom bokeh.plotting import figure, show, output_notebook\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%matplotlib inline\nplt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\noutput_notebook()\ntrain_dir = \"../input\"\ntrain_file = \"train.csv\"\n\nfbcheckin_train_tbl = pd.read_csv(os.path.join(train_dir, train_file))\nfbcheckin_train_tbl = fbcheckin_train_tbl.sort_values(by=\"place_id\")\nsample_fbcheckin_train_tbl = fbcheckin_train_tbl[:10000]\nplt.scatter(sample_fbcheckin_train_tbl[\"x\"], sample_fbcheckin_train_tbl[\"y\"], c=sample_fbcheckin_train_tbl[\"place_id\"])\nplt.title(\"Places sample distribution over (x,y)\")\n\n\n"
 }
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