polars.LazyFrame.melt#

LazyFrame.melt(id_vars: Optional[Union[str, list[str]]] = None, value_vars: Optional[Union[str, list[str]]] = None, variable_name: Optional[str] = None, value_name: Optional[str] = None) → LDF[source]#

Unpivot a DataFrame from wide to long format.

Optionally leaves identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters:

id_vars: Columns to use as identifier variables.
value_vars: Values to use as identifier variables. If value_vars is empty all columns that are not in id_vars will be used.
variable_name: Name to give to the value column. Defaults to “variable”
value_name: Name to give to the value column. Defaults to “value”

Examples

>>> df = pl.DataFrame(
...     {
...         "a": ["x", "y", "z"],
...         "b": [1, 3, 5],
...         "c": [2, 4, 6],
...     }
... ).lazy()
>>> df.melt(id_vars="a", value_vars=["b", "c"]).collect()
shape: (6, 3)
┌─────┬──────────┬───────┐
│ a   ┆ variable ┆ value │
│ --- ┆ ---      ┆ ---   │
│ str ┆ str      ┆ i64   │
╞═════╪══════════╪═══════╡
│ x   ┆ b        ┆ 1     │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ y   ┆ b        ┆ 3     │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ z   ┆ b        ┆ 5     │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ x   ┆ c        ┆ 2     │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ y   ┆ c        ┆ 4     │
├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ z   ┆ c        ┆ 6     │
└─────┴──────────┴───────┘