Analyze Text – Strings and Basic File Handling**

Tools You’ll Use

• Python 3 (Colab, Jupyter, or interpreter)
• Code editor/IDE (VS Code, Thonny, PyCharm, or text editor)
• Sample text files (or just paste text directly)

Lesson Roadmap

1. Why text matters in programming.
2. String basics: storing and manipulating text.
3. Useful string methods: .lower(), .split(), .count().
4. Plan a word counter: count words, characters, and frequencies.
5. Work with files: reading from and writing to .txt.
6. Error handling: what if the file doesn’t exist?
7. Real-world applications: essays, blogs, coding interviews.
8. Recap & quiz.
9. Mini challenge: exclude stopwords, save results neatly.

Why Strings Matter

Most of the world’s data is text — books, emails, tweets, logs. As a programmer, you’ll often process text: searching for words, cleaning it, or counting it.

A string in Python is just text inside quotes:

sentence = "Python is fun!"

👉 Intuition tip: Think of strings as a necklace of beads, where each bead is a character. You can count them, split them into pieces, or rearrange them.

Step 1: String Basics

text = "Hello World"

print(text.lower())   # "hello world"
print(text.upper())   # "HELLO WORLD"
print(len(text))      # 11 characters
print(text.split())   # ['Hello', 'World']
print(text.count("o"))  # 2

Common string tasks:

• .lower() → normalize case (important for comparisons).
• .split() → break into words.
• .count(word) → count appearances.
• len() → count characters.

Step 2: Plan the Word Counter

We want to analyze text by:

1. Counting words.
2. Counting characters.
3. Finding the most frequent word.
4. Saving results to a file.

Step 3: Writing the Code

text = input("Paste some text: ")

# Convert to lowercase for consistency
text = text.lower()

# Split into words
words = text.split()

# Count total words and characters
word_count = len(words)
char_count = len(text)

# Find most frequent word
freq = {}
for word in words:
    freq[word] = freq.get(word, 0) + 1

most_common = max(freq, key=freq.get)

print("Total words:", word_count)
print("Total characters:", char_count)
print("Most frequent word:", most_common, "(", freq[most_common], "times )")

Step 4: File Handling

Python can read and write text files.

Reading:

with open("sample.txt", "r") as f:
    data = f.read()
    print(data)

Writing:

with open("results.txt", "w") as f:
    f.write("Word count: " + str(word_count))

👉 Tip: Always use with open(...) — it auto-closes files and prevents memory leaks.

Sample Run

Input:

Data science is fun. Python makes data science easier.

Output:

Total words: 8
Total characters: 52
Most frequent word: data (2 times)

Common Mistakes & Fixes

• Forgetting .lower() → “Data” and “data” are treated as different words.
• Splitting without cleanup → punctuation like “fun.” stays attached. (You can later use regex for cleaning.)
• File not found → use try/except to handle missing files gracefully.

👉 Debugging tip: Print words[:10] to quickly see how text was split.

Real-World Applications

• Writers checking word count for essays.
• Bloggers analyzing common words in posts.
• Social media analysts finding most-used hashtags.
• Programmers parsing logs to detect errors.

This project introduces the basics of text analytics, which power advanced fields like Natural Language Processing (NLP).

Recap of Key Concepts

• Strings: store text data and have useful methods.
• Counting words/characters: simple but powerful text analysis.
• Dictionaries: used here to track frequencies.
• File handling: reading/writing text to store results.

Review Questions

1. Why do we convert text to lowercase before counting?
2. What happens if you try to read a file that doesn’t exist?
3. Which data structure is best for counting word frequencies?

Mini Challenges

1. Stopword removal: Ignore words like “the”, “is”, “and”.
2. Top 5 words: Print not just the most common, but the top 5 frequent words.
3. Save analysis: Write results (word count, top words) into a new file.
4. Punctuation cleanup: Remove .,!? before splitting words.