{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"092c6a14-e282-8dd1-92a2-dcea424b1b73"},"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":"bfba116c-e7f8-3b17-6954-690e81088730"},"outputs":[],"source":"train_date_part = pd.read_csv('../input/train_date.csv', nrows=100)\ntrain_numeric_part = pd.read_csv('../input/train_numeric.csv',nrows=100)\ntrain_cat_part = pd.read_csv('../input/train_categorical.csv',nrows=100)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e4addc7b-d07f-19bd-2219-d9be359b972a"},"outputs":[],"source":"train_date_part.head(3)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"a1f36311-2346-5e02-1b27-17c11f52e5b3"},"outputs":[],"source":"train_numeric_part.head(3)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c430e488-e4d5-20fc-2825-16e8a95c8ea8"},"outputs":[],"source":"train_cat_part.head(3)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"feeb69aa-db93-8e24-3aa7-ba69838e3425"},"outputs":[],"source":"cat0=train_cat_part[:1]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"4436b9ad-d096-dd9c-5ac1-12077cd72612"},"outputs":[],"source":"cat0.fillna(0)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"525bdb3c-d46d-54f3-8ead-2c87ed2bc615"},"outputs":[],"source":"cat0a = np.array(cat0.fillna(0))[0]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"13131ca2-196b-8b60-118a-fed78b34745f"},"outputs":[],"source":"cat0a=cat0a[1:]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1b3a9b1a-786a-f97d-ebc2-b9951b7f3928"},"outputs":[],"source":"cat0a[cat0a!=0.]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"50b6b39b-7928-fadd-a297-8829722e4229"},"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}