polars.DataFrame.transpose#

DataFrame.transpose( *, include_header: bool = False, header_name: str = 'column', column_names: str | Iterable[str] | None = None, ) → DataFrame[source]#

Transpose a DataFrame over the diagonal.

Parameters:

include_header: If set, the column names will be added as first column.
header_name: If include_header is set, this determines the name of the column that will be inserted.
column_names: Optional iterable yielding strings or a string naming an existing column. These will name the value (non-header) columns in the transposed data.

Returns:

DataFrame

Notes

This is a very expensive operation. Perhaps you can do it differently.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df.transpose(include_header=True)
shape: (2, 4)
┌────────┬──────────┬──────────┬──────────┐
│ column ┆ column_0 ┆ column_1 ┆ column_2 │
│ ---    ┆ ---      ┆ ---      ┆ ---      │
│ str    ┆ i64      ┆ i64      ┆ i64      │
╞════════╪══════════╪══════════╪══════════╡
│ a      ┆ 1        ┆ 2        ┆ 3        │
│ b      ┆ 4        ┆ 5        ┆ 6        │
└────────┴──────────┴──────────┴──────────┘

Replace the auto-generated column names with a list

>>> df.transpose(include_header=False, column_names=["x", "y", "z"])
shape: (2, 3)
┌─────┬─────┬─────┐
│ x   ┆ y   ┆ z   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 2   ┆ 3   │
│ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┘

Include the header as a separate column

>>> df.transpose(
...     include_header=True, header_name="foo", column_names=["x", "y", "z"]
... )
shape: (2, 4)
┌─────┬─────┬─────┬─────┐
│ foo ┆ x   ┆ y   ┆ z   │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╪═════╡
│ a   ┆ 1   ┆ 2   ┆ 3   │
│ b   ┆ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┴─────┘

Replace the auto-generated column with column names from a generator function

>>> def name_generator():
...     base_name = "my_column_"
...     count = 0
...     while True:
...         yield f"{base_name}{count}"
...         count += 1
>>> df.transpose(include_header=False, column_names=name_generator())
shape: (2, 3)
┌─────────────┬─────────────┬─────────────┐
│ my_column_0 ┆ my_column_1 ┆ my_column_2 │
│ ---         ┆ ---         ┆ ---         │
│ i64         ┆ i64         ┆ i64         │
╞═════════════╪═════════════╪═════════════╡
│ 1           ┆ 2           ┆ 3           │
│ 4           ┆ 5           ┆ 6           │
└─────────────┴─────────────┴─────────────┘

Use an existing column as the new column names

>>> df = pl.DataFrame(dict(id=["i", "j", "k"], a=[1, 2, 3], b=[4, 5, 6]))
>>> df.transpose(column_names="id")
shape: (2, 3)
┌─────┬─────┬─────┐
│ i   ┆ j   ┆ k   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 2   ┆ 3   │
│ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┘
>>> df.transpose(include_header=True, header_name="new_id", column_names="id")
shape: (2, 4)
┌────────┬─────┬─────┬─────┐
│ new_id ┆ i   ┆ j   ┆ k   │
│ ---    ┆ --- ┆ --- ┆ --- │
│ str    ┆ i64 ┆ i64 ┆ i64 │
╞════════╪═════╪═════╪═════╡
│ a      ┆ 1   ┆ 2   ┆ 3   │
│ b      ┆ 4   ┆ 5   ┆ 6   │
└────────┴─────┴─────┴─────┘