{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"47fef9c5-a99b-7273-8c7b-8b74c22e6c40"},"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)\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\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"960509a0-ffbf-b9d4-3a67-9b985884d350"},"outputs":[],"source":"filename = \"train_categorical.csv\"\nfilename = \"test_date.csv\"\nf = open(\"../input/\" + filename, \"r\")\ns = []\nfor i in range(10):\n    s.append(f.readline())\nf.close()\nprint(\"\\n\\n\".join(s[:2]))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b3eb704d-c07f-302f-8f80-51b022c9e4fe"},"outputs":[],"source":"s[2]"}],"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}