<jupyter_start><jupyter_text>Lab 3  Question 1<jupyter_code>num_list = [12,32,43,35]
num_list.sort()
print(num_list[-1])
print(num_list[0])<jupyter_output>43
12<jupyter_text>Question 2<jupyter_code>str_lab = 'this is my third python lab.'.split()
print (len(str_lab))<jupyter_output>6<jupyter_text>Question 3<jupyter_code>my_list = ['a', '123', 123, 'b', 'B', 'False', False, 123, None, 'None']
print (len(set(my_list)))<jupyter_output>9<jupyter_text>Question 4<jupyter_code>my_dict = {"favorite_count":1138,
    "lang":"ch",
        "coordinates":(-75.14310264, 40.05701649),
            "visited_countries":["GR","HK","MY"]}
print (my_dict)<jupyter_output>{'favorite_count': 1138, 'lang': 'ch', 'coordinates': (-75.14310264, 40.05701649), 'visited_countries': ['GR', 'HK', 'MY']}<jupyter_text>4.1<jupyter_code>my_dict['lang'] = 'en'<jupyter_output><empty_output><jupyter_text>4.2<jupyter_code>print (len(my_dict['visited_countries']))<jupyter_output>3<jupyter_text>4.3<jupyter_code>my_dict['visited_countries'].append('CH')
print (my_dict)<jupyter_output>{'favorite_count': 1138, 'lang': 'en', 'coordinates': (-75.14310264, 40.05701649), 'visited_countries': ['GR', 'HK', 'MY', 'CH']}<jupyter_text>4.4<jupyter_code>print ('US' in my_dict['visited_countries'])<jupyter_output>False<jupyter_text>4.5<jupyter_code>print (my_dict['coordinates'][0])<jupyter_output>-75.14310264<jupyter_text>4.6<jupyter_code>my_dict['coordinates']=(-81,45)
print (my_dict)<jupyter_output>{'favorite_count': 1138, 'lang': 'en', 'coordinates': (-81, 45), 'visited_countries': ['GR', 'HK', 'MY', 'CH']}
{'fraction_non_alphanumeric': 0.20962634578847372, 'fraction_numerical': 0.08739708676377454, 'mean_word_length': 10.703703703703704, 'pattern_counts': {'":': 4, '<': 32, '<?xml version=': 0, '>': 32, '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': '10', 'dir': 'jupyter-structured-clean-dedup', 'id': '258290', 'license': 'MIT', 'n_tokens_mistral': 780, 'n_tokens_neox': 688, 'n_words': 81, 'path': 'Lab 3.ipynb', 'repo_name': 'tommylogan9/IA-241', 'size': '1579'}