{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"450a0127-3c77-6039-4e56-0d66820d74cf"},"outputs":[],"source":"import pandas\n\n# Creating one chunk of 1000 row from each data set and write it to file to work on\nchunk_size = 10 ** 3\nprint('Starting')\nfor data in pandas.read_csv('../input/train_date.csv', iterator=True, chunksize=chunk_size):\n    data.to_csv('date_sample.csv')\n    break\nfor data in pandas.read_csv('../input/train_categorical.csv', iterator=True, chunksize=chunk_size):\n    data.to_csv('../input/categorical_sample.csv')\n    break\nfor data in pandas.read_csv('../input/train_numeric.csv', iterator=True, chunksize=chunk_size):\n    data.to_csv('../input/numeric_sample.csv')\n    break"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2fa930b2-a4a1-b030-552a-aa9e31c6bc3a"},"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}