{"cells":[{"metadata":{},"cell_type":"markdown","source":"# What's parquet file\n\nI have not use with `parquet` file and do not know how to use it.\n\nFor now, I want to retrieve image information."},{"metadata":{},"cell_type":"markdown","source":"# Data info\n\nhttps://www.kaggle.com/c/bengaliai-cv19/data\n\n(train/test).parquet\nEach parquet file contains tens of thousands of 137x236 grayscale images. The images have been provided in the parquet format for I/O and space efficiency. Each row in the parquet files contains an image_id column, and the flattened image."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 in \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 \"../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        fullpath = os.path.join(dirname, filename)\n        print('{}:{} MB'.format(fullpath, round(os.path.getsize(fullpath) / (1024.0 ** 2), 1)))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Load data\n\nIt takes a long time to read because the data size is large."},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_image_0 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_0.parquet')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data size"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_image_0.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Row info\n\n`137 * 236 = 32332`\n\n0 ~ 255 grayscale data\n\nwith `image_id`"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_image_0.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Image num\n50210"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_image_0.index","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Remove image_id."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_image_data = train_image_0.drop('image_id', axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_image_data.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Create image data"},{"metadata":{"trusted":true},"cell_type":"code","source":"IMAGE_ROW = 137 \nIMAGE_COLUMN = 236\n\nimg0 = train_image_data[0:1].values[0].reshape([IMAGE_ROW, IMAGE_COLUMN])\nimg0","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Show image"},{"metadata":{"trusted":true},"cell_type":"code","source":"from matplotlib import pylab as plt\n\nplt.imshow(img0)","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":1}