{"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":"2023-05-15T18:37:12.023032Z","iopub.execute_input":"2023-05-15T18:37:12.023791Z","iopub.status.idle":"2023-05-15T18:37:12.097282Z","shell.execute_reply.started":"2023-05-15T18:37:12.023747Z","shell.execute_reply":"2023-05-15T18:37:12.096322Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install pyarrow","metadata":{"execution":{"iopub.status.busy":"2023-05-15T18:39:53.77608Z","iopub.execute_input":"2023-05-15T18:39:53.776542Z","iopub.status.idle":"2023-05-15T18:40:07.825116Z","shell.execute_reply.started":"2023-05-15T18:39:53.776493Z","shell.execute_reply":"2023-05-15T18:40:07.823806Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pyarrow.parquet as pq\n\n# Read a Parquet file\ntable = pq.read_table('/kaggle/input/asl-fingerspelling/train_landmarks/1358493307.parquet')\n\n# Convert the table to a pandas DataFrame\ndf = table.to_pandas()\n\n# Access the data in the DataFrame\nprint(df.head())\n","metadata":{"execution":{"iopub.status.busy":"2023-05-15T18:41:23.383913Z","iopub.execute_input":"2023-05-15T18:41:23.384329Z","iopub.status.idle":"2023-05-15T18:41:27.468249Z","shell.execute_reply.started":"2023-05-15T18:41:23.384267Z","shell.execute_reply":"2023-05-15T18:41:27.466481Z"},"trusted":true},"execution_count":null,"outputs":[]}]}