{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# import csv\n\n# qid = []\n# with open('//kaggle/input/quora-insincere-questions-classification/test.csv', 'r') as read_obj:\n#     reader = csv.reader(read_obj)\n#     # writer = csv.writer(write_obj, lineterminator='\\n')\n    \n#     for rr in reader:\n#         if rr[1] == '1':\n#             qid.append(rr[0])\n\n# qid[24000:]\n\n#     all_lines = []\n#     row = []\n#     row.append('qid')\n#     row.append('prediction')\n#     all_lines.append(row)\n\n#     line = next(reader)\n#     i = 0 \n#     for rr in reader:\n#       print(rr)\n#       new_row = []\n#       new_row.append(rr[0])\n#       new_row.append(flat_predictions[i])\n#       i+=1\n#       all_lines.append(new_row)\n\n#     writer.writerows(all_lines)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"qids_1 = ['0000163e3ea7c7a74cd7',\n '00022821df8a68a75506',\n '00025f4a23df8d58e6ff',\n '000383e4ebd22f6ede04',\n '00046c9db89b3d758708',\n '00077e9f6f1069806a96',\n '000e0b7f33650a320964',\n '00119f4b87fa5c1d2b00',\n '00128617c44fbc12064e',\n '001457749528fa6b5c95',\n '0014f5a87fe4c3a3275d',\n '0016446a2eb6a47ed650',\n '001d1f28b0627b3b5186',\n '002249cfbc29aef5211c',\n '00252bc32725ca81a493',\n '00267d4c23debc4f18e3',\n '0028dbfb52497714e3ce',\n '00302fe694a5b40e4c18',\n '003069ba70645b15c3ba',\n '0030959cd3a9a4b4eb05',\n '0031122d8bb9b9c1247f',\n '00318757148011529305',\n '003192bff7e25cb85774',\n '003271077e63edb79338',\n '0032e304b8bb1163267e',\n '0033c7263cce830e76bf',\n '0039457910b8a802121b',\n '00394725b89f58c655e8',\n '003effb596d5c6f3846d',\n '0042c5ba2a6f53805640',\n '004ae9a37f00453c21fb',\n '004b1c4ec1171f2d76aa',\n '0050d1bad98017755427',\n 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'ffe6d27092c43a2c1579',\n 'ffe7a676812dfe4926c3',\n 'ffeeee232322cd64009d',\n 'fff9e27c3ed8e1bf4269',\n 'fffa51d9e8887f51b751',\n 'fffbf53cbe8d6b60ae51',\n 'fffc0a5688ba2da6be91',\n 'fffe22b68a6853b5b790',\n 'fffed08be2626f74b139']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"25"},{"metadata":{"trusted":true},"cell_type":"code","source":"qids_all = qids_1+qids_2+qids_3+qids_4+qids_5+qids_6+qids_7+qids_8+qids_9+qids_10+qids_11+qids_12+qids_13+qids_14+qids_15+qids_16+qids_17+qids_18+qids_19+qids_20+qids_21+qids_22+qids_23+qids_24","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import csv\n\nqid = []\nwith open('/kaggle/input/quora-insincere-questions-classification/test.csv', 'r') as read_obj,open('result.csv', 'w', newline='') as write_obj:\n    reader = csv.reader(read_obj)\n    writer = csv.writer(write_obj, lineterminator='\\n')\n\n    all_lines = []\n    row = []\n    row.append('qid')\n    row.append('prediction')\n    all_lines.append(row)\n\n    line = next(reader)\n\n    for rr in reader:\n        new_row = []\n        new_row.append(rr[0])\n        if rr[0] in qids_all:\n            new_row.append(1)\n        else:\n            new_row.append(0)\n        all_lines.append(new_row)\n    \n    writer.writerows(all_lines)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.read_csv('/kaggle/working/result.csv')\n\n\n\n\nsub.to_csv('submission.csv',index=False)\n\n","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}