{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":50160,"databundleVersionId":7602123,"sourceType":"competition"}],"dockerImageVersionId":30664,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"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 numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2024-03-10T02:05:07.215684Z","iopub.execute_input":"2024-03-10T02:05:07.216166Z","iopub.status.idle":"2024-03-10T02:05:07.222900Z","shell.execute_reply.started":"2024-03-10T02:05:07.216123Z","shell.execute_reply":"2024-03-10T02:05:07.221821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_sta_0_0 = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_static_0_0.csv\")\ntrain_sta_0_1 = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_static_0_1.csv\")\ntrain_base    = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_base.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-03-10T02:05:07.224533Z","iopub.execute_input":"2024-03-10T02:05:07.224931Z","iopub.status.idle":"2024-03-10T02:05:41.564518Z","shell.execute_reply.started":"2024-03-10T02:05:07.224883Z","shell.execute_reply":"2024-03-10T02:05:41.563159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_static_0 = pd.concat([train_sta_0_0,train_sta_0_1],axis=0).reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2024-03-10T02:05:41.566296Z","iopub.execute_input":"2024-03-10T02:05:41.566651Z","iopub.status.idle":"2024-03-10T02:05:48.595635Z","shell.execute_reply.started":"2024-03-10T02:05:41.566621Z","shell.execute_reply":"2024-03-10T02:05:48.594717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_depth_0 = pd.merge(data_static_0, train_base, on='case_id', how='inner')","metadata":{"execution":{"iopub.status.busy":"2024-03-10T02:05:48.596756Z","iopub.execute_input":"2024-03-10T02:05:48.597075Z","iopub.status.idle":"2024-03-10T02:05:50.651156Z","shell.execute_reply.started":"2024-03-10T02:05:48.597048Z","shell.execute_reply":"2024-03-10T02:05:50.649954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 特徵numinstpaidearly3d_3546850L 和 numinstlallpaidearly3d_817L 相關係數為 1\n","metadata":{}},{"cell_type":"code","source":"sns.scatterplot(x='numinstpaidearly3d_3546850L', y='numinstlallpaidearly3d_817L', data=train_depth_0)\n\nplt.xlabel('numinstpaidearly3d_3546850L')\nplt.ylabel('numinstlallpaidearly3d_817L')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-10T02:05:50.652536Z","iopub.execute_input":"2024-03-10T02:05:50.652905Z","iopub.status.idle":"2024-03-10T02:05:53.120802Z","shell.execute_reply.started":"2024-03-10T02:05:50.652876Z","shell.execute_reply":"2024-03-10T02:05:53.119837Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}