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row['average_rating']\n        print('Changed entries for row with bookID {}'.format(row['bookID']))\n\nod_output.add_data('output', book_data)\n","timeout":300,"tablename":""},"result":null,"options":{"configEditExpertModeEnabled":false,"x":350,"y":151,"name":"Python-Script Dual Input","color":"#e91e63"},"out":{"output":{"f568838d-9340-4d68-95b7-642f0485887f":"dataframe1"}}},"0e81e01a-bc58-41a6-b9bb-1fe91cfc5143":{"id":"0e81e01a-bc58-41a6-b9bb-1fe91cfc5143","processor":"00000000-0000-0000-0001-000000000004","config":{"dataLocation":{"reference":{"type":null,"id":null}},"parseMode":["FAIL_FAST"],"tablename":""},"result":null,"options":{"configEditExpertModeEnabled":false,"x":350,"y":-71,"name":"Data Table 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book_data)\n","timeout":300,"tablename":""},"result":null,"options":{"configEditExpertModeEnabled":false,"x":375,"y":381,"name":"Python-Script Dual Input","color":"#e91e63"},"out":{"output":{"a1768ce7-5cb6-40e3-83fa-7d32f40638b1":"dataframe1"}}},"a1768ce7-5cb6-40e3-83fa-7d32f40638b1":{"id":"a1768ce7-5cb6-40e3-83fa-7d32f40638b1","processor":"00000000-0000-0000-0065-000000000003","config":{"multipleModelLoad":{"triggerState":true,"values":{}},"inputName1":"bookData","inputName2":"addedRows","generateEmptyDatasetOutput":false,"multipleModelSave":{"triggerState":true,"values":{}},"manuallyAdded":[{"id":"448ad6ef-bdb1-4918-8cf9-861cff344a5c","name":"text_reviews_count","scaleType":"nominal","representationType":"numeric"},{"id":"5f64b6ec-06ae-4a04-aee9-7a6dca3dc04a","name":"average_rating","scaleType":"nominal","representationType":"numeric"},{"id":"e59ee8c8-d6a0-45ab-b453-bfbf27897671","name":"ratings_count","scaleType":"nominal","representationType":"numeric"},{"id":"3d349833-fb12-495a-8a42-19a2c546a372","name":"__num_pages","scaleType":"nominal","representationType":"numeric"},{"id":"6226fb4f-7da7-472a-9bcc-1b58c69337ab","name":"isbn13","scaleType":"nominal","representationType":"numeric"}],"script":"from onelogic.odpf import ImageType\nimport pandas as pd\n\n# od_input keys represent names of the input datasets set in OD processor  \nbook_data = od_input['bookData'].get_as_pandas()\nadded_rows = od_input['addedRows'].get_as_pandas()\n\nmax_book_ID = book_data['bookID'].max()\n\nif 'bookID' in added_rows.columns:\n    for index, row in added_rows.iterrows():\n        max_book_ID += 1\n        new_row = {\n            '__num_pages': row['__num_pages'],\n            'text_reviews_count': 1234,\n            'title': row['title'],\n            'ratings_count': row['ratings_count'],\n            'language_code': row['language_code'],\n            'isbn13': 1234,\n            'isbn': row['isbn'],\n            'average_rating': row['average_rating'],\n            'authors': row['authors'],\n            'bookID': max_book_ID\n        }\n        book_data = book_data.append(new_row, ignore_index=True)\n        \n\nod_output.add_data(\"output\", book_data)\n\n","timeout":300,"tablename":""},"result":null,"options":{"configEditExpertModeEnabled":false,"x":399,"y":672,"name":"Python-Script Dual 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