Intermediate

Missing Data & Transformations

Finding and filling gaps, plus the vectorized way to change values.

Call	Behavior	Example
`df.dropna()`	drop rows with ANY missing value	a 3-row df with 1 NaN row → 2 rows left
`df.dropna(thresh=2)`	keep rows with at least 2 non-missing values	row with only 1 non-null value gets dropped, others kept
`df.fillna(0)`	fill every gap with a constant	`[1, NaN, 3]` → `[1, 0, 3]`
`df.interpolate()`	fill gaps by linear interpolation	`[1, NaN, 3]` → `[1, 2.0, 3]`

For transforming values, vectorized operations beat .apply() almost every time: .apply() runs a real Python function call per row/element, which is much slower than an operation NumPy/pandas can run in a tight C loop.

Now practice it

Exercises that use what this lesson just covered.

Rows with Missing AgeEasy Change a Single ValueEasy Map yes/no to BooleanEasy Replace a ValueEasy Cast Specific ColumnsEasy Drop Sparse Rows with a ThresholdMedium

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