Transpose a DataFrame over the diagonal.
Description
Transpose a DataFrame over the diagonal.
Usage
<DataFrame>$transpose(
include_header = FALSE,
header_name = "column",
column_names = NULL
)
Arguments
include_header
|
If TRUE, the column names will be added as first column.
|
header_name
|
If include_header is TRUE, this determines the
name of the column that will be inserted.
|
column_names
|
Character vector indicating the new column names. If NULL
(default), the columns will be named as "column_1", "column_2", etc. The
length of this vector must match the number of rows of the original
input.
|
Details
This is a very expensive operation.
Transpose may be the fastest option to perform non foldable (see
fold() or reduce()) row operations like
median.
Polars transpose is currently eager only, likely because it is not trivial to deduce the schema.
Value
DataFrame
Examples
library(polars)
# simple use-case
pl$DataFrame(mtcars)$transpose(include_header = TRUE, column_names = rownames(mtcars))
#> shape: (11, 33)
#> ┌────────┬───────────┬─────────────┬────────┬───┬─────────────┬─────────┬─────────────┬────────────┐
#> │ column ┆ Mazda RX4 ┆ Mazda RX4 ┆ Datsun ┆ … ┆ Ford ┆ Ferrari ┆ Maserati ┆ Volvo 142E │
#> │ --- ┆ --- ┆ Wag ┆ 710 ┆ ┆ Pantera L ┆ Dino ┆ Bora ┆ --- │
#> │ str ┆ f64 ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ f64 │
#> │ ┆ ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ │
#> ╞════════╪═══════════╪═════════════╪════════╪═══╪═════════════╪═════════╪═════════════╪════════════╡
#> │ mpg ┆ 21.0 ┆ 21.0 ┆ 22.8 ┆ … ┆ 15.8 ┆ 19.7 ┆ 15.0 ┆ 21.4 │
#> │ cyl ┆ 6.0 ┆ 6.0 ┆ 4.0 ┆ … ┆ 8.0 ┆ 6.0 ┆ 8.0 ┆ 4.0 │
#> │ disp ┆ 160.0 ┆ 160.0 ┆ 108.0 ┆ … ┆ 351.0 ┆ 145.0 ┆ 301.0 ┆ 121.0 │
#> │ hp ┆ 110.0 ┆ 110.0 ┆ 93.0 ┆ … ┆ 264.0 ┆ 175.0 ┆ 335.0 ┆ 109.0 │
#> │ drat ┆ 3.9 ┆ 3.9 ┆ 3.85 ┆ … ┆ 4.22 ┆ 3.62 ┆ 3.54 ┆ 4.11 │
#> │ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
#> │ qsec ┆ 16.46 ┆ 17.02 ┆ 18.61 ┆ … ┆ 14.5 ┆ 15.5 ┆ 14.6 ┆ 18.6 │
#> │ vs ┆ 0.0 ┆ 0.0 ┆ 1.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 1.0 │
#> │ am ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 1.0 ┆ 1.0 │
#> │ gear ┆ 4.0 ┆ 4.0 ┆ 4.0 ┆ … ┆ 5.0 ┆ 5.0 ┆ 5.0 ┆ 4.0 │
#> │ carb ┆ 4.0 ┆ 4.0 ┆ 1.0 ┆ … ┆ 4.0 ┆ 6.0 ┆ 8.0 ┆ 2.0 │
#> └────────┴───────────┴─────────────┴────────┴───┴─────────────┴─────────┴─────────────┴────────────┘
# All rows must have one shared supertype, recast Categorical to String which is a supertype
# of f64, and then dataset "Iris" can be transposed
pl$DataFrame(iris)$with_columns(pl$col("Species")$cast(pl$String))$transpose()
#> shape: (5, 150)
#> ┌──────────┬──────────┬──────────┬──────────┬───┬────────────┬────────────┬────────────┬───────────┐
#> │ column_0 ┆ column_1 ┆ column_2 ┆ column_3 ┆ … ┆ column_146 ┆ column_147 ┆ column_148 ┆ column_14 │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ 9 │
#> │ str ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ --- │
#> │ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ str │
#> ╞══════════╪══════════╪══════════╪══════════╪═══╪════════════╪════════════╪════════════╪═══════════╡
#> │ 5.1 ┆ 4.9 ┆ 4.7 ┆ 4.6 ┆ … ┆ 6.3 ┆ 6.5 ┆ 6.2 ┆ 5.9 │
#> │ 3.5 ┆ 3.0 ┆ 3.2 ┆ 3.1 ┆ … ┆ 2.5 ┆ 3.0 ┆ 3.4 ┆ 3.0 │
#> │ 1.4 ┆ 1.4 ┆ 1.3 ┆ 1.5 ┆ … ┆ 5.0 ┆ 5.2 ┆ 5.4 ┆ 5.1 │
#> │ 0.2 ┆ 0.2 ┆ 0.2 ┆ 0.2 ┆ … ┆ 1.9 ┆ 2.0 ┆ 2.3 ┆ 1.8 │
#> │ setosa ┆ setosa ┆ setosa ┆ setosa ┆ … ┆ virginica ┆ virginica ┆ virginica ┆ virginica │
#> └──────────┴──────────┴──────────┴──────────┴───┴────────────┴────────────┴────────────┴───────────┘