Map values

Map values — `mapValues`

Map column values to new values via a lookup (CASE-WHEN-style), with an optional default for unmapped values.

Use cases

Recode grades to outcomes (A/B → Pass).
Translate codes to labels, sending anything unmapped to a default.

What it does

Looks up each value in the mapping table and replaces it. With use_default: true, anything not in the mapping gets the default value.

Before

student	grade
Alice	A
Bob	C
Carol	B
Dave	F

4 rows · 2 cols

Map values (mapping: A→Pass, B→Pass; default=Fail; new_column=result)

After

student	grade	resultnew
Alice	A	Pass
Bob	C	Fail
Carol	B	Pass
Dave	F	Fail

4 rows · 3 cols

Configuration

Config key	Type	Required	Description
`column`	string	Yes	Column whose values are mapped
`mapping`	object	Yes	`{ "value": "becomes" }`
`new_column`	string	No	Write to a new column (empty = overwrite `column`)
`default`	any	No	Value for anything not in the mapping
`use_default`	bool	No	When `true`, unmapped values become `default`. If omitted, it's inferred: `true` when a `default` value is set, `false` otherwise

Generated Python code

# mapping: {"A": "Pass", "B": "Pass"}, default "Fail"
df_2 = df_1.assign(result=lambda _d: _d['grade'].map({'A': 'Pass', 'B': 'Pass'}).where(_d['grade'].isin(['A', 'B']), 'Fail'))

Tips & common mistakes

Setting a default automatically enables it — you only need use_default: false explicitly if you want unmapped values to pass through unchanged despite having set a default.
Set new_column to keep the original column alongside the recoded one.
For one-to-one substitutions (with optional regex) prefer Replace values; for if/else logic over several columns, use Conditional column.