{"cells":[
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  "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\n#print('\tReading train.csv')\ndf_train = pd.read_csv('./data/train.csv',\n                        usecols=['row_id','x','y','time','place_id','accuracy'], \n                        index_col = 0)\n#print('\tReading test.csv')\ndf_test = pd.read_csv('./data/test.csv',\n                        usecols=['row_id','x','y','time','accuracy'],\n                        index_col = 0)\n\n# Any results you write to the current directory are saved as output.\n"
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