{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"1c7eda95-125e-fb13-947a-12081b4affbb"},"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"06f8c6d7-857c-0e4c-65f7-84a376fc45df"},"outputs":[],"source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nimport time"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f60bfa6f-a873-7172-5d03-7d34d7364c2d"},"outputs":[],"source":"#Borrowed from Faron's Road to 4 kernel\nDATA_DIR = \"../input\"\n\nID_COLUMN = 'Id'\nTARGET_COLUMN = 'Response'\n\nSEED = 0\nCHUNKSIZE = 50000\nNROWS = 500000\n\nTRAIN_NUMERIC = \"{0}/train_numeric.csv\".format(DATA_DIR)\nTRAIN_DATE = \"{0}/train_date.csv\".format(DATA_DIR)\n\nTEST_NUMERIC = \"{0}/test_numeric.csv\".format(DATA_DIR)\nTEST_DATE = \"{0}/test_date.csv\".format(DATA_DIR)\n\n#Borrowed from Faron's Road to 4 kernel\n\ntrain = pd.read_csv(TRAIN_NUMERIC, usecols=[ID_COLUMN, TARGET_COLUMN], nrows=NROWS)\ntest = pd.read_csv(TEST_NUMERIC, usecols=[ID_COLUMN], nrows=NROWS)\n\ntrain[\"StartTime\"] = -1\ntest[\"StartTime\"] = -1"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"4bc9b894-5043-473c-c281-fcbf1307c6e3"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.5.2"}},"nbformat":4,"nbformat_minor":0}