{"cells": [{"execution_count": null, "outputs": [], "metadata": {"collapsed": false, "_uuid": "dc9d5310a1f8c7a727faf1797057aab140be53b2", "_execution_state": "idle"}, "source": "", "cell_type": "markdown"}, {"execution_count": null, "cell_type": "code", "metadata": {"_execution_state": "idle", "trusted": false, "_uuid": "148e633310e58e143289bdd3c93065031f9fa592", "_cell_guid": "314efb74-b02d-4a64-b2b3-a1747159f080"}, "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.", "outputs": []}, {"execution_count": null, "outputs": [], "metadata": {"collapsed": false, "_uuid": "6189652568c602774b5f562f92020592849359b5", "_execution_state": "idle"}, "source": "metadata_file = \"../input/metadata.csv\"\n\nmetadata = open(metadata_file, 'r');\n\nwhile(1):\n    line = metadata.readline();\n    if (len(line) == 0):\n        break;\n\n    array = line.split(\",\")\n\n    if (array[3] != array[4] and len(array[3]) > 2 and len(array[4]) > 2):\n        print(array)\n", "cell_type": "code"}], "nbformat": 4, "metadata": {"kernelspec": {"name": "python3", "display_name": "Python 3", "language": "python"}, "language_info": {"name": "python", "version": "3.6.1", "codemirror_mode": {"name": "ipython", "version": 3}, "mimetype": "text/x-python", "file_extension": ".py", "nbconvert_exporter": "python", "pygments_lexer": "ipython3"}}, "nbformat_minor": 0}