{"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","execution":{"iopub.status.busy":"2022-07-23T02:14:23.927420Z","iopub.execute_input":"2022-07-23T02:14:23.928757Z","iopub.status.idle":"2022-07-23T02:14:23.940285Z","shell.execute_reply.started":"2022-07-23T02:14:23.928701Z","shell.execute_reply":"2022-07-23T02:14:23.939097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')\ndata.head(10)","metadata":{"execution":{"iopub.status.busy":"2022-07-23T02:14:40.607519Z","iopub.execute_input":"2022-07-23T02:14:40.607893Z","iopub.status.idle":"2022-07-23T02:14:40.661840Z","shell.execute_reply.started":"2022-07-23T02:14:40.607861Z","shell.execute_reply":"2022-07-23T02:14:40.660624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"columns = [\"LotFrontage\",\"LotArea\",\"SalePrice\"]\ndata_numeric = data.loc[:,columns]\n\ndata_numeric","metadata":{"execution":{"iopub.status.busy":"2022-07-23T02:18:12.501811Z","iopub.execute_input":"2022-07-23T02:18:12.502242Z","iopub.status.idle":"2022-07-23T02:18:12.522493Z","shell.execute_reply.started":"2022-07-23T02:18:12.502209Z","shell.execute_reply":"2022-07-23T02:18:12.521580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"selected_rows = data_numeric[data_numeric['LotFrontage'].isnull()]\nprint('Selected rows')\nprint(selected_rows)","metadata":{"execution":{"iopub.status.busy":"2022-07-23T02:19:17.942646Z","iopub.execute_input":"2022-07-23T02:19:17.943069Z","iopub.status.idle":"2022-07-23T02:19:17.954944Z","shell.execute_reply.started":"2022-07-23T02:19:17.943030Z","shell.execute_reply":"2022-07-23T02:19:17.953725Z"},"trusted":true},"execution_count":null,"outputs":[]}]}