{"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":7921029,"sourceType":"competition"}],"dockerImageVersionId":30673,"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","execution":{"iopub.status.busy":"2024-03-25T22:47:55.503781Z","iopub.execute_input":"2024-03-25T22:47:55.505602Z","iopub.status.idle":"2024-03-25T22:47:56.894705Z","shell.execute_reply.started":"2024-03-25T22:47:55.505544Z","shell.execute_reply":"2024-03-25T22:47:56.893591Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pandas import read_parquet","metadata":{"execution":{"iopub.status.busy":"2024-03-25T22:58:41.242913Z","iopub.execute_input":"2024-03-25T22:58:41.243356Z","iopub.status.idle":"2024-03-25T22:58:41.249679Z","shell.execute_reply.started":"2024-03-25T22:58:41.243325Z","shell.execute_reply":"2024-03-25T22:58:41.248008Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"file_path = \"/kaggle/input/home-credit-credit-risk-model-stability/parquet_files/train/train_base.parquet\"","metadata":{"execution":{"iopub.status.busy":"2024-03-25T22:59:01.164648Z","iopub.execute_input":"2024-03-25T22:59:01.165360Z","iopub.status.idle":"2024-03-25T22:59:01.171004Z","shell.execute_reply.started":"2024-03-25T22:59:01.165325Z","shell.execute_reply":"2024-03-25T22:59:01.169500Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data = read_parquet(file_path)","metadata":{"execution":{"iopub.status.busy":"2024-03-25T22:59:27.838933Z","iopub.execute_input":"2024-03-25T22:59:27.839325Z","iopub.status.idle":"2024-03-25T22:59:28.254835Z","shell.execute_reply.started":"2024-03-25T22:59:27.839296Z","shell.execute_reply":"2024-03-25T22:59:28.253219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data","metadata":{"execution":{"iopub.status.busy":"2024-03-25T22:59:34.378588Z","iopub.execute_input":"2024-03-25T22:59:34.380419Z","iopub.status.idle":"2024-03-25T22:59:34.410715Z","shell.execute_reply.started":"2024-03-25T22:59:34.380355Z","shell.execute_reply":"2024-03-25T22:59:34.409457Z"},"trusted":true},"execution_count":null,"outputs":[]}]}