Module 5: Data Manipulation

Cleaning is just the start. "Manipulation" means reshaping, transforming, and extracting new value from your data using custom logic.

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Lesson 10: Sorting & Ranking

Sorting

• df.sort_values(by="col"): Sorts by a column. ascending=False for descending order.
• df.sort_index(): Sorts by the index labels.

Ranking

• df.rank(): Assigns a rank (1, 2, 3...) to values. Useful for "Top N" analysis.

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Lesson 11: Applying Functions

The most powerful tool for custom transformations.

1. apply()

Applies a custom function to every Row or Column.

• df.apply(func, axis=0): Down columns (default).
• df.apply(func, axis=1): Across rows.

2. map()

Used on Series to map values to new ones (like a VLOOKUP).

df['GenderNum'] = df['Gender'].map({'Male': 0, 'Female': 1})

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Lesson 12: String Manipulation

Pandas has a .str accessor that gives you all standard string methods (upper, lower, replace, split) on an entire column at once!

• df['Name'].str.upper()
• df['Phone'].str.replace("-", "")
• df['Email'].str.contains("@gmail.com")

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Practice: Data Transformer

Challenge:

1. Create a DataFrame with names: ["Alice", "BOB", "CharLie"].
2. Use .str to convert them all to Title Case (e.g., "Alice", "Bob", "Charlie").
3. Create a new column "Length" that contains the length of each name (using apply or .str.len()).

Key Takeaways

✅ sort_values organizes your data.
✅ apply lets you run Python logic on DataFrame rows.
✅ Always use .str for text columns; do not loop over strings!