<jupyter_start><jupyter_text>3<jupyter_code>path = {os.path.basename(x): x for x in glob.glob(os.path.join("D:\Projects\Covid19\data\Original_cropped\COVID", '*.png'))}
covid_data = []
count = int("30806")
for img in path:
    covid_data.append((img, "Covid", count))
    count += 1

covid_data = pd.DataFrame(covid_data, columns=['Image Index', 'Finding Labels', 'Patient ID'])

covid_data['Paths'] = covid_data['Image Index'].map(path.get)
covid_data.head()
covid_data.to_csv('covid.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18461538461538463, 'fraction_numerical': 0.01730769230769231, 'mean_word_length': 6.661764705882353, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '153325', 'license': 'MIT', 'n_tokens_mistral': 218, 'n_tokens_neox': 210, 'n_words': 33, 'path': 'Classification/src/preprocessing/Splitting Data.ipynb', 'repo_name': 'kool7/COVID-19-Segmentation', 'size': '520'}