<jupyter_start><jupyter_text>Table of Contents1&nbsp;&nbsp;Question1.1&nbsp;&nbsp;Answer2&nbsp;&nbsp;Question2.1&nbsp;&nbsp;Answer  Question**Question 1 :**Write a function to compute 5/0 and use try/except to catch the exceptions.  Answer<jupyter_code>def division(Numarator, Denominator):
    result = int()
    try:
        result = Numarator/Denominator
        return result
    except ZeroDivisionError:
        return 'Provide Denominator Greater than : 0'
print(division(5, 0))<jupyter_output>Provide Denominator Greater than : 0<jupyter_text>Question**Question 2**Implement a Python program to generate all sentences where subject is in ["Americans", "Indians"]verb is in    ["Play", "watch"]object is in  ["Baseball","cricket"].**Hint:** Subject,Verb and Object should be declared in the program as shown below. `subjects  = ["Americans","Indians"]``verbs     = ["play","watch"]``objects   = ["Baseball","Cricket"]`**Output should come as below:**Americans play Baseball.Americans play Cricket.Americans watch Baseball.Americans watch Cricket.Indians play Baseball.Indians play Cricket.Indians watch Baseball.Indians watch Cricket.  Answer<jupyter_code>subjects  = ["Americans", "Indians"]
verbs     = ["play", "watch"]
objects   = ["Baseball", "Cricket"]
for i in [i+ ' ' +j+' ' +k for i in subjects for j in verbs for k in objects]:
    print(i)<jupyter_output>Americans play Baseball
Americans play Cricket
Americans watch Baseball
Americans watch Cricket
Indians play Baseball
Indians play Cricket
Indians watch Baseball
Indians watch Cricket
{'fraction_non_alphanumeric': 0.11118251928020566, 'fraction_numerical': 0.008997429305912597, 'mean_word_length': 5.4875, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '615915', 'license': 'MIT', 'n_tokens_mistral': 479, 'n_tokens_neox': 442, 'n_words': 150, 'path': 'Assignments/Basic-Python-Assignment-5-EH.ipynb', 'repo_name': 'vigneshpalanivelr/MeachineLearningAI', 'size': '1556'}