Question

Write a program that determines and prints the number of duplicate words in a sentence. Treat uppercase and lowercase letters the same. Ignore punctuation.

Short answer

Use text normalization, remove punctuation, split into words, count occurrences, identify duplicates, and print the count.

Step-by-step solution

Normalize the Sentence

First, convert the entire sentence to lowercase to ensure that the comparison is case-insensitive. Use Python's built-in `lower()` method for this process.

Remove Punctuation

To handle punctuation, use the `string` module in Python to access a list of punctuation characters and remove these from the sentence using a loop or a regular expression.

Split the Sentence into Words

After cleaning the sentence from punctuation, split it into individual words. This can be accomplished with the `split()` method that divides a string into a list based on spaces.

Count Each Word

Initialize a dictionary or use the `collections.Counter` to count the occurrences of each word in the list. This will allow you to determine the frequency of each word.

Identify Duplicates

Iterate through the dictionary and count words that have more than one occurrence. These are the duplicates.

Print the Result

Finally, print the count of duplicate words identified in the previous step.

Key concepts

Text Normalization

Text normalization is a crucial step when processing textual data in Python programming. It ensures consistency by transforming the text into a standard format.
The first thing to do is convert all characters to lowercase, which eliminates any issues with case sensitivity. This is done using the `lower()` method in Python, which swiftly changes any uppercase letters to lowercase ones.
Following that, it's essential to tackle punctuation removal. Text data often contain various punctuation marks that do not contribute to the meaning in terms of word counting. Using Python's `string` module, you can access and remove these characters with either loops or regular expressions. This results in a "cleaner" version of the text, focusing solely on the words themselves.
Remember, by normalizing the text, we prepare it for accurate processing in subsequent steps, such as counting word frequency or detecting duplicates.

Word Frequency

In text analysis, determining how often each word appears is fundamental, especially in tasks like duplicate words detection. Once your text is normalized and split into words, the next step is to count the occurrences of each word.
In Python, you can choose between using a dictionary or leveraging the `collections.Counter` class to track word counts easily. A dictionary allows you to manually increase the count of each word every time it reappears.
On the other hand, `collections.Counter` provides a more efficient and hassle-free alternative by automating this process. It generates a special dictionary-like object where each unique word is a key, and its frequency is the associated value.

• Use a loop to iterate through the list of words.
• Either increment a count in a dictionary or use `Counter` for automatic counting.

By getting a firm grasp on word frequency, you can understand text composition and quickly identify patterns, such as which words are most likely to appear multiple times.

Duplicate Words Detection

Once you've established word frequency, detecting duplicates becomes straightforward. In the context of your Python program, a duplicate word is any word that appears more than once in the given text.
With your word frequency data, you only need to filter out words with a count greater than one. Loop through your word-count dictionary or the `Counter` object to find these instances.
Here's a simple approach:

• Initialize a counter for duplicate words.
• Iterate over the word-frequency dictionary.
• If the frequency of a word exceeds one, increase your duplicate word counter.

Finally, print the number of words that were repeated. This gives you a clear view of text redundancy, which can be crucial for applications like text optimization or data cleaning. Knowing how to handle duplicates lets you refine your analysis and improve the accuracy of your conclusions.