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How To Python Read Txt File And Count Words?
2025-07-07 05:20:31
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3 Answers
Ivy
2025-07-13 02:33:04
I remember the first time I needed to count words in a text file using Python. It was for a small personal project, and I was amazed at how simple it could be. I opened the file using 'open()' with the 'r' mode for reading. Then, I used the 'read()' method to get the entire content as a single string. Splitting the string with 'split()' gave me a list of words, and 'len()' counted them. I also learned to handle file paths properly and close the file with 'with' to avoid resource leaks. This method works well for smaller files, but for larger ones, I later discovered more efficient ways like reading line by line.
Eva
2025-07-10 05:46:09
Counting words in a text file with Python is a common task, and there are several ways to do it depending on your needs. The simplest approach is to use 'open()' to read the file, then 'split()' the content into words and 'len()' to count them. For example:
with open('example.txt', 'r') as file:
words = file.read().split()
word_count = len(words)
This works fine for small files, but for larger files, you might want to read the file line by line to save memory. You can also use libraries like 're' to handle punctuation and special characters better. Another tip is to strip whitespace and handle edge cases like empty files.
If you need more advanced features, like counting the frequency of each word, you can use 'collections.Counter'. This gives you a dictionary with words as keys and counts as values. It's a powerful tool for text analysis. Remember to always close your files or use 'with' to manage resources automatically.
with open('example.txt', 'r') as file:
words = file.read().split()
word_count = len(words)
This works fine for small files, but for larger files, you might want to read the file line by line to save memory. You can also use libraries like 're' to handle punctuation and special characters better. Another tip is to strip whitespace and handle edge cases like empty files.
If you need more advanced features, like counting the frequency of each word, you can use 'collections.Counter'. This gives you a dictionary with words as keys and counts as values. It's a powerful tool for text analysis. Remember to always close your files or use 'with' to manage resources automatically.
Mckenna
2025-07-08 21:17:28
When I started learning Python, one of the first things I wanted to do was analyze text files. Counting words seemed like a great starting point. I found that Python makes it incredibly straightforward. You can open the file, read its content, and split it into words with just a few lines of code. For instance:
with open('story.txt', 'r') as f:
text = f.read()
words = text.split()
print(len(words))
This basic method works for most cases, but I soon realized it doesn't account for punctuation or hyphenated words. To handle those, I learned about regular expressions with the 're' module. Splitting on '\W+' helped ignore non-word characters. For larger files, reading in chunks or lines is better to avoid memory issues. Python's flexibility makes it easy to adapt the solution to different needs.
with open('story.txt', 'r') as f:
text = f.read()
words = text.split()
print(len(words))
This basic method works for most cases, but I soon realized it doesn't account for punctuation or hyphenated words. To handle those, I learned about regular expressions with the 're' module. Splitting on '\W+' helped ignore non-word characters. For larger files, reading in chunks or lines is better to avoid memory issues. Python's flexibility makes it easy to adapt the solution to different needs.
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process(line)
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