<jupyter_start><jupyter_text>Q1<jupyter_code>num_list= [12,32,43,35]
num_list.sort()
print (num_list[0])
print (num_list[-1])<jupyter_output>12
43<jupyter_text>Q2<jupyter_code>str= "this is my third python lab"
print (str.split())
print (len(str.split()))<jupyter_output>['this', 'is', 'my', 'third', 'python', 'lab']
6<jupyter_text>Q3<jupyter_code>my_list= ['a','123',123,'b','B','False',False,123,None,'None']
print (set(my_list))
print (len(set(my_list)))<jupyter_output>{False, 'None', None, '123', 'False', 'B', 'a', 'b', 123}
9<jupyter_text>Q4<jupyter_code>my_dict= {"favorite_count": 1138,
    "lang":"ch", 
        "coordinates": (-75.14310264, 40.05701649),
            "visited_countries":["GR","HK","MY"]}<jupyter_output><empty_output><jupyter_text>4.1<jupyter_code>my_dict["lang"]="en"
print (my_dict)<jupyter_output>{'favorite_count': 1138, 'lang': 'en', 'coordinates': (-75.14310264, 40.05701649), 'visited_countries': ['GR', 'HK', 'MY', 'CH']}<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': 'ch', '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': 'ch', 'coordinates': (-81, 45), 'visited_countries': ['GR', 'HK', 'MY', 'CH']}
{'fraction_non_alphanumeric': 0.22879330943847073, 'fraction_numerical': 0.08542413381123058, 'mean_word_length': 11.050359712230216, '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': '200990', 'license': 'MIT', 'n_tokens_mistral': 830, 'n_tokens_neox': 737, 'n_words': 90, 'path': 'Lab3.ipynb', 'repo_name': 'AKinkead/IA241', 'size': '1674'}