No, 9.11 is not greater than 9.9.

Here's how to compare them:

1. Look at the whole number part: Both numbers have 9 before the decimal point, so they are equal so far.
2. Look at the tenths place (the first digit after the decimal):
   • 9.11 has a 1 in the tenths place.
   • 9.9 has a 9 in the tenths place.
3. Compare those digits: Since 1 is less than 9, the number 9.11 is less than 9.9.

Therefore, 9.9 is greater than 9.11.

No, 9.11 is less than 9.9.

This is because 9.11 is equivalent to 9.110, and 9.9 is equivalent to 9.900. Since 9.110 is smaller than 9.900, 9.9 is greater than 9.11.

Okay, here are 5 jokes:

1. Why don't scientists trust atoms? Because they make up everything!

2. I told my wife she was drawing her eyebrows too high. She looked surprised.

3. What do you call a lazy kangaroo? Pouch potato!

4. Want to hear a joke about construction? I'm still working on it.

5. Why did the scarecrow win an award? Because he was outstanding in his field!

How many are unique? All 5 of the jokes generated above are unique from each other.

Here are five jokes for you:

1. Why did the scarecrow win an award?
   Because he was outstanding in his field!

2. Why don’t skeletons fight each other?
   They don’t have the guts!

3. Why did the math book look sad?
   It had too many problems.

4. Why don’t eggs tell jokes?
   Because they might crack up!

5. What did one ocean say to the other ocean?
   Nothing, they just waved.

Now, let's check how many of these jokes are unique. I'll analyze them for similarity.

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity

List of jokes

jokes = [ "Why did the scarecrow win an award? Because he was outstanding in his field!", "Why don’t skeletons fight each other? They don’t have the guts!", "Why did the math book look sad? It had too many problems.", "Why don’t eggs tell jokes? Because they might crack up!", "What did one ocean say to the other ocean? Nothing, they just waved." ]

Vectorize the jokes

vectorizer = TfidfVectorizer().fit_transform(jokes) similarity_matrix = cosine_similarity(vectorizer)

Count unique jokes (jokes with low similarity to others)

unique_jokes = set() threshold = 0.5 # Define similarity threshold

for i in range(len(jokes)): is_unique = True for j in range(len(jokes)): if i != j and similarity_matrix[i, j] > threshold: is_unique = False break if is_unique: unique_jokes.add(jokes[i])

Count unique jokes

num_unique_jokes = len(unique_jokes) num_unique_jokes

All 5 jokes are unique! None of them are too similar to each other. 🎉