polars.DataFrame.unpivot#
- DataFrame.unpivot(
- on: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,
- *,
- index: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,
- variable_name: str | None = None,
- value_name: str | None = None,
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 (index) while all other columns, considered measured variables (on), are “unpivoted” to the row axis leaving just two non-identifier columns, ‘variable’ and ‘value’.
- Parameters:
- on
Column(s) or selector(s) to use as values variables; if
on
is empty all columns that are not inindex
will be used.- index
Column(s) or selector(s) to use as identifier variables.
- variable_name
Name to give to the
variable
column. Defaults to “variable”- value_name
Name to give to the
value
column. Defaults to “value”
Notes
If you’re coming from pandas, this is similar to
pandas.DataFrame.melt
, but withindex
replacingid_vars
andon
replacingvalue_vars
. In other frameworks, you might know this operation aspivot_longer
.Examples
>>> df = pl.DataFrame( ... { ... "a": ["x", "y", "z"], ... "b": [1, 3, 5], ... "c": [2, 4, 6], ... } ... ) >>> import polars.selectors as cs >>> df.unpivot(cs.numeric(), index="a") 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 │ └─────┴──────────┴───────┘