count, countDistinct, and count_if are aggregate functions for counting rows in a Spark Scala DataFrame. count counts rows or non-null values, countDistinct counts unique values, and count_if counts rows that match a condition.

The bitwise shift functions move the bits of an integer column left or right by a fixed number of positions. They're useful for packing and unpacking flags, fast multiplication or division by powers of two, and working with binary protocol data.

e() returns Euler's number (≈ 2.71828) and pi() returns π (≈ 3.14159). They're handy when a Spark expression needs one of these mathematical constants — for circle math, exponential growth, trigonometry, and so on — without you having to hard-code the literal value.

width_bucket assigns a numeric value to an equiwidth histogram bucket given a range and a bucket count. It's the right tool when you need to bin continuous values into fixed-size groups — age brackets, price tiers, score ranges — without writing a chain of when expressions.

The pmod function returns the positive remainder of dividing one column by another. Unlike the standard % operator, which mirrors the sign of the dividend, pmod keeps the result non-negative whenever the divisor is positive. That makes it the right tool for hash bucketing and any cyclic indexing where a negative remainder would point you at the wrong bucket.

The hypot function computes sqrt(a² + b²) for two numeric inputs without the intermediate overflow or underflow that a naive implementation would produce. It's the standard tool for distances between points, vector magnitudes, and anywhere the Pythagorean theorem applies.

signum returns -1.0 for negative numbers, 0.0 for zero, and 1.0 for positive numbers. It's useful when you care about the direction of a value but not its magnitude — flagging gains vs. losses, classifying deltas, or branching on the sign of a difference. Spark also exposes the SQL-only aliases sign, negative, and positive for working with the sign of a column.

The factorial function returns the factorial of an integer column — the product of all positive integers up to and including the input value (n! = n × (n-1) × ... × 2 × 1). It's useful anywhere you need to compute permutations, combinations, or other counting expressions inline in a DataFrame.

The degrees and radians functions convert DataFrame columns between the two ways of measuring angles. radians turns degrees into radians; degrees does the reverse. They're the unit-conversion helpers you reach for whenever your data is in degrees but you need to feed it into Spark's trig functions, which all expect radians.

Spark Scala exposes the full set of trigonometric functions from java.lang.Math as DataFrame column functions: the basics (sin, cos, tan), their inverses (asin, acos, atan, atan2), the reciprocals (cot, csc, sec), and the hyperbolic versions of all of them. Every input and output is in radians, not degrees — use the radians function to convert if your data is in degrees.