{"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 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 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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls ../input/firstsubmission","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport os \n\nmy_result = pd.read_csv(os.path.join('/kaggle/input/firstsubmission', 'submission.csv'), index_col=False)\nsam_submi = pd.read_csv(os.path.join('/kaggle/input/deepfake-detection-challenge','sample_submission.csv'), index_col=False)\nnew_result = pd.merge(sam_submi, my_result, on='filename', how='left')\nnew_result.fillna(0, inplace=True)\nnew_result['label'] = new_result['label_y']\nnew_result = new_result[['filename', 'label']]\nnew_result['label'] = new_result['label'].astype('int64')\nnew_result.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"ls","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"import pandas as pd\nsubmission = pd.read_csv(\"../input/firstsubmission/submission.csv\")\nsubmission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submitted = pd.read_csv('../input/firstsubmission/submission.csv')\n\nsubmission = pd.DataFrame(submitted, columns=['filename', 'label']).fillna(0.5)\nsubmission.sort_values('filename').to_csv('submission.csv', index=False)\nsubmission.to_csv('submission.csv', index=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nfile = open(\"/kaggle/input/deepfake-detection-challenge/sample_submission.csv\")\nfile2 = open('../input/firstsubmission/submission.csv', 'r')\n\nfor line in file:\n    tocheck = str(line).split(',')\n    for secondline in file2:\n        if 'filename' not in str(secondline) and str(tocheck[0]) in secondline:\n            print('yes')\n\n# file.close()\n# file2.close()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\nfrom pandas import *\n\noriginal = pd.read_csv(\"/kaggle/input/deepfake-detection-challenge/sample_submission.csv\")\nsubmitted = pd.read_csv('../input/firstsubmission/submission.csv')\nfor i, row in enumerate(original.values):\n    filename, label = row\n    for x, rows in enumerate(submitted.values):\n        file, lable = rows\n        if str(filename) == str(file):\n            original.replace(to_replace =label,  \n                            value =lable) \n            #label = lable\n\noriginal.to_csv('newsubmission.csv', index=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from pandas import *\nimport pandas as pd\n\noriginal = pd.read_csv(\"/kaggle/input/deepfake-detection-challenge/sample_submission.csv\")\nsubmitted = pd.read_csv('../input/firstsubmission/submission.csv')\noriginalar = []\nsubmittedar = []\nfor i, row in enumerate(original.values):\n    filename, label = row\n    originalar.append(filename)\nfor x, rows in enumerate(submitted.values):\n    file, lable = rows\n    print(lable)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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":1}