<jupyter_start><jupyter_text>Sorting for binary classification using only AP Images<jupyter_code>chexpert_AP = chexpert_train[chexpert_train['AP/PA'] == 'AP']
chexpert_AP
for path in chexpert_AP.index:
    source_path = os.path.join(PATH_TO_CHEXPERT_DOWNLOADS, chexpert_AP.loc[path]['Path'])

    if chexpert_AP.loc[path]['No Finding'] == 1.0:
        target_path = os.rename(source_path, os.path.join(PATH_TO_NORMAL_TRAIN_DIRECTORY_AP, str(chexpert_AP.loc[path]['Path']).replace('/', '_')))
    else:
        target_path = os.rename(source_path, os.path.join(PATH_TO_ABNORMAL_TRAIN_DIRECTORY_AP, str(chexpert_AP.loc[path]['Path']).replace('/', '_')))<jupyter_output><empty_output><jupyter_text>Sorting for binary classification using only PA Images<jupyter_code>chexpert_PA = chexpert_train[chexpert_train['AP/PA'] == 'PA']
chexpert_PA
for path in chexpert_PA.index:
    source_path = os.path.join(PATH_TO_CHEXPERT_DOWNLOADS, chexpert_PA.loc[path]['Path'])

    if chexpert_PA.loc[path]['No Finding'] == 1.0:
        target_path = os.rename(source_path, os.path.join(PATH_TO_NORMAL_TRAIN_DIRECTORY_PA, str(chexpert_PA.loc[path]['Path']).replace('/', '_')))
    else:
        target_path = os.rename(source_path, os.path.join(PATH_TO_ABNORMAL_TRAIN_DIRECTORY_PA, str(chexpert_PA.loc[path]['Path']).replace('/', '_')))<jupyter_output><empty_output><jupyter_text>Sorting for binary classification using only L images<jupyter_code>chexpert_L = chexpert_train[chexpert_train['Frontal/Lateral'] == 'Lateral']
chexpert_L
for path in chexpert_L.index:
    source_path = os.path.join(PATH_TO_CHEXPERT_DOWNLOADS, chexpert_L.loc[path]['Path'])

    if chexpert_L.loc[path]['No Finding'] == 1.0:
        target_path = os.rename(source_path, os.path.join(PATH_TO_NORMAL_TRAIN_DIRECTORY_L, str(chexpert_L.loc[path]['Path']).replace('/', '_')))
    else:
        target_path = os.rename(source_path, os.path.join(PATH_TO_ABNORMAL_TRAIN_DIRECTORY_L, str(chexpert_L.loc[path]['Path']).replace('/', '_')))<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '633155', 'license': 'MIT', 'n_tokens_mistral': 783, 'n_tokens_neox': 779, 'n_words': 94, 'path': 'datasetsforeverything/AkshatChexpertBinarySort.ipynb', 'repo_name': 'akshat2048/MSOE_ML', 'size': '2018'}