<jupyter_start><jupyter_text>Instructions:(1) Load ICD category code descriptions into dataframe. Do not store the file on disk.  url: https://github.com/kamillamagna/ICD-10-CSV/blob/master/categories.csv?raw=true(2) Load ICD block descriptions into a dataframe. Do no store any files to disk  url: https://www.aapc.com/icd-10/  hint: consider using `pd.read_html`(3) Count all the icd10 codes characterized as diseases according to the ICD block description(4) From the codes in step (3) isolate though refering to 'viral' or 'virus' and concatenate, separating by semicolon<jupyter_code>import pandas as pd
url='https://raw.githubusercontent.com/kamillamagna/ICD-10-CSV/master/categories.csv'
category_code = pd.read_csv(url,sep=",")  
category_code
url=''
block_desc = pd.read_csv(url,sep=",")  
block_desc<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11084624553039332, 'fraction_numerical': 0.015494636471990465, 'mean_word_length': 5.774193548387097, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 3, 'lorem ipsum': 0, 'www.': 1, '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': '633824', 'license': 'MIT', 'n_tokens_mistral': 291, 'n_tokens_neox': 274, 'n_words': 80, 'path': 'ICD10_EDA.ipynb', 'repo_name': 'nedaresa/EDA_ICD10', 'size': '839'}