{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"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)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd.read_csv('/kaggle/input/store-sales-time-series-forecasting/train.csv')\n\ntrain = pd.read_csv('/kaggle/input/codebreak-10-store-sales-time-series-forecasting/train.csv')\ntest = pd.read_csv('/kaggle/input/codebreak-10-store-sales-time-series-forecasting/test.csv')\nstores = pd.read_csv('/kaggle/input/codebreak-10-store-sales-time-series-forecasting/stores.csv')\noil = pd.read_csv('/kaggle/input/codebreak-10-store-sales-time-series-forecasting/oil.csv')\nholidays = pd.read_csv('/kaggle/input/codebreak-10-store-sales-time-series-forecasting/holidays_events.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T20:18:20.467557Z","iopub.execute_input":"2023-05-16T20:18:20.468109Z","iopub.status.idle":"2023-05-16T20:18:20.936929Z","shell.execute_reply.started":"2023-05-16T20:18:20.468049Z","shell.execute_reply":"2023-05-16T20:18:20.935125Z"},"trusted":true},"execution_count":1,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m test \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m stores \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstores.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n","\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"],"ename":"NameError","evalue":"name 'pd' is not defined","output_type":"error"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}