{"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_minor":4,"nbformat":4,"cells":[{"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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data1 = pd.read_csv(\"../input/cost4504/submission (10).csv\")\ndata2 = pd.read_csv(\"../input/external-best-18apr/submission 4522.csv\")\ndata3 = pd.read_csv(\"../input/external-best-18apr/submission_4555.csv\")\ndata4 = pd.read_csv(\"../input/external-best-18apr/submission_4_483.csv\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data1 = data1.sort_values(by=['site_path_timestamp'],ascending=True).reset_index(drop=True)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data2 = data2.sort_values(by=['site_path_timestamp'],ascending=True).reset_index(drop=True)\ndata3 = data3.sort_values(by=['site_path_timestamp'],ascending=True).reset_index(drop=True)\ndata4 = data4.sort_values(by=['site_path_timestamp'],ascending=True).reset_index(drop=True)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_total = data2\ndata_total['x'] =0.20*data1['x']+ 0.05*data2['x']+ 0.05*data3['x']+ 0.70*data4['x']\ndata_total['y'] =0.20*data1['y']+ 0.05*data2['y']+ 0.05*data3['y']+ 0.70*data4['y']","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_total.to_csv(\"submission.csv\",index=False)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}