{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"a127fac1-056f-35fa-8ff9-6ce2ff127316"},"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":"3123aa41-6eab-bc59-3ccf-927cd191026f"},"outputs":[],"source":"train_numeric = pd.read_csv('../input/train_numeric.csv', nrows=1000)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ddb48d7d-fc4e-cafe-5420-b6970fd3ab14"},"outputs":[],"source":"train_numeric.shape"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"77189305-15a2-0c0f-4f5b-78d3f47ba16e"},"outputs":[],"source":"train_numeric.head()"}],"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}