{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","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)\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":"2023-09-07T14:41:15.023724Z","iopub.execute_input":"2023-09-07T14:41:15.024146Z","iopub.status.idle":"2023-09-07T14:41:19.295939Z","shell.execute_reply.started":"2023-09-07T14:41:15.024111Z","shell.execute_reply":"2023-09-07T14:41:19.295036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"****WE WILL GO DEEPER INTO THE DATA IN THE UPCOMING VERSIONS**\n\n**PLEASE COMMENT DOWN IF I DID ANY MISTAKES, OR IF CAN MAKE THIS MORE CONNECTED TO THE GROUND, OR SUGGESTIONS. YOUR ASSISTS ARE HIGHLY APPRECIABLE****","metadata":{}},{"cell_type":"code","source":"import os ","metadata":{"execution":{"iopub.status.busy":"2023-09-07T14:41:19.297463Z","iopub.execute_input":"2023-09-07T14:41:19.297755Z","iopub.status.idle":"2023-09-07T14:41:19.303590Z","shell.execute_reply.started":"2023-09-07T14:41:19.297729Z","shell.execute_reply":"2023-09-07T14:41:19.302619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir('/kaggle/input/predict-ai-model-runtime/npz_all/npz/layout/xla/random/train')","metadata":{"execution":{"iopub.status.busy":"2023-09-07T14:41:19.304696Z","iopub.execute_input":"2023-09-07T14:41:19.305002Z","iopub.status.idle":"2023-09-07T14:41:19.319557Z","shell.execute_reply.started":"2023-09-07T14:41:19.304975Z","shell.execute_reply":"2023-09-07T14:41:19.318747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n","metadata":{"execution":{"iopub.status.busy":"2023-09-07T14:41:19.321472Z","iopub.execute_input":"2023-09-07T14:41:19.322676Z","iopub.status.idle":"2023-09-07T14:41:19.327724Z","shell.execute_reply.started":"2023-09-07T14:41:19.322633Z","shell.execute_reply":"2023-09-07T14:41:19.326663Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport os\n\n# Specify the directory containing the DataFrame file(s)\ndata_directory = '/kaggle/input/predict-ai-model-runtime/npz_all/npz/tile/xla/'\n\n# Initialize a list to store the loaded DataFrames\ndataframes = []\n\n# Iterate through files in the directory\nfor filename in os.listdir(data_directory):\n    if filename.endswith('.csv'):  # Assuming your data is in CSV format\n        # Load the DataFrame from the CSV file\n        data = pd.read_csv(os.path.join(data_directory, filename))\n        \n        # Append the loaded DataFrame to the list\n        dataframes.append(data)\n\n# Now, dataframes contains the loaded DataFrames from all the CSV files in the directory\n","metadata":{"execution":{"iopub.status.busy":"2023-09-07T14:41:19.329281Z","iopub.execute_input":"2023-09-07T14:41:19.329664Z","iopub.status.idle":"2023-09-07T14:41:19.339145Z","shell.execute_reply.started":"2023-09-07T14:41:19.329627Z","shell.execute_reply":"2023-09-07T14:41:19.338200Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\ndata_directory = '/kaggle/input/predict-ai-model-runtime/npz_all/npz/tile/xla/'\n\n# List all files and subdirectories in the specified directory\ncontents = os.listdir(data_directory)\n\n# Print the contents\nfor item in contents:\n    # Check if it's a file or a subdirectory\n    if os.path.isfile(os.path.join(data_directory, item)):\n        print(f\"File: {item}\")\n    else:\n        print(f\"Directory: {item}\")\n","metadata":{"execution":{"iopub.status.busy":"2023-09-07T14:41:19.340528Z","iopub.execute_input":"2023-09-07T14:41:19.341197Z","iopub.status.idle":"2023-09-07T14:41:19.350196Z","shell.execute_reply.started":"2023-09-07T14:41:19.341166Z","shell.execute_reply":"2023-09-07T14:41:19.349178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}],"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"}}