Python Project: Text Processing an Important Skill

Implement text processing as a Python developer

As a Python developer, it is important to be proficient in text processing because text data is ubiquitous in the modern world.

Many applications, websites, and platforms rely on text data as a primary source of information.

Text processing involves manipulating and analyzing text data, which can range from simple operations like counting the frequency of words in a document to more complex tasks like natural language processing and sentiment analysis.

Here are a few reasons why text processing is important for Python developers:

1. Data analysis. Text processing is an important part of data analysis. Python developers can use text processing techniques to extract valuable insights from large volumes of unstructured data.
2. Automation. Python can be used to automate many repetitive tasks that involve text processing, such as cleaning and standardizing text data.
3. Natural Language Processing (NLP). NLP is a rapidly growing field that involves analyzing and understanding natural language data. Python is a popular language for NLP due to its extensive libraries and tools for text processing.
4. Machine learning. Text data is often used as input for machine learning models. Python has a wide range of machine learning libraries that are well-suited for working with text data.

Overall, text processing is a critical skill for Python developers who want to work with data, automate tasks, or build applications that involve natural language processing or machine learning.

Project Description

Consider the file files/bachelor.txt.

What are the most likely words after the name Holmes occurs in the text.

Examples

...
     friend Sherlock Holmes had a considerable share in clearing
...
     still sharing rooms with Holmes in Baker Street, that he
...

In the two examples the word after Holmes is had and in.

How to solve this

There are many ways to solve this problem, but first, let’s understand some of the obvious challenges.

• The text is in lines, and Holmes might be the last word on a line.
• There might be symbols after Holmes, like commas, punctuation, or similar.
• Uppercase and lowercase words should probably count as the same word.

Step 1 Read and split the content into words

A great way to deal with the issue of having multiple lines is to divide them into words.

Luckily, Python has made that easy for you to read files.

# Read all the content
filename = 'https://raw.githubusercontent.com/LearnPythonWithRune/Python-Projects/main/files/bachelor.txt'
with open(filename) as f:
    content = f.read()
# Split it into words
words = content.split()

Now you have a list of words.

Step 2 Simplifying and counting

The next step could be to investigate the words.

You will notice words in uppercase, words with quotes, or whatever you want to remove.

Counting occurrences can be done easily with dictionaries.

freq = {}
# Used to record if the previous word was Holmes
last_word_holmes = False
# Iterate over all words
for word in words:
    # If last word was Holmes
    if last_word_holmes:
        last_word_holmes = False
        
        # Remove special characters
        word = word.replace("'", '').replace('"', '')
        word = word.replace(',', '').replace('.', '')
        word = word.lower()  # Change to lowercase
        
        # Update the number of occurences
        freq[word] = freq.get(word, 0) + 1
    
    if 'Holmes' in word:
        last_word_holmes = True

Step 3 Displaying the counts

Let’s say we only care about words occuring more than one time.

Then we can do that as follows.

for word, count in freq.items():
    if count > 1:
        print(word, count)

This example has showed you how to process words easily by using split() and use replace() to remove characters.

Finally, how to use a dictionary to keep count.

Want more Python projects?

This is part of 19 Python Projects and you can create an acronym generator and master 5 key skills as a programmer.

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