{"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":71549,"databundleVersionId":8561470,"sourceType":"competition"}],"dockerImageVersionId":30715,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"https://github.com/paul-reiners/pneumonia-detection-chest-x-rays/blob/main/EDA.ipynb","metadata":{}},{"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","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nfrom glob import glob\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n##Import any other packages you may need here\nimport pydicom\nimport glob\nfrom itertools import chain\nimport matplotlib.image as mpimg","metadata":{"execution":{"iopub.status.busy":"2024-05-30T22:46:43.662198Z","iopub.execute_input":"2024-05-30T22:46:43.662650Z","iopub.status.idle":"2024-05-30T22:46:47.081276Z","shell.execute_reply.started":"2024-05-30T22:46:43.662617Z","shell.execute_reply":"2024-05-30T22:46:47.080324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train.csv')\ntrain_df.sample(3)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T22:52:18.272607Z","iopub.execute_input":"2024-05-30T22:52:18.273388Z","iopub.status.idle":"2024-05-30T22:52:18.322094Z","shell.execute_reply.started":"2024-05-30T22:52:18.273296Z","shell.execute_reply":"2024-05-30T22:52:18.320209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_series_descriptions_df = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_series_descriptions.csv')\ntrain_series_descriptions_df.sample(3)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T22:58:24.368186Z","iopub.execute_input":"2024-05-30T22:58:24.368663Z","iopub.status.idle":"2024-05-30T22:58:24.400295Z","shell.execute_reply.started":"2024-05-30T22:58:24.368632Z","shell.execute_reply":"2024-05-30T22:58:24.399146Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_label_coordinates_df = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_label_coordinates.csv')\ntrain_label_coordinates_df.sample(3)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T22:56:29.699394Z","iopub.execute_input":"2024-05-30T22:56:29.699877Z","iopub.status.idle":"2024-05-30T22:56:29.847926Z","shell.execute_reply.started":"2024-05-30T22:56:29.699828Z","shell.execute_reply":"2024-05-30T22:56:29.846944Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Explore 2D Imaging Properties","metadata":{}},{"cell_type":"code","source":"all_xray_df['Finding Labels'].unique()","metadata":{},"execution_count":null,"outputs":[]}]}