export¶

pydiverse.transform.export( target: Target, *, schema_overrides: dict[str, Any] | None = None, ) → Pipeable[source]¶

Convert a pydiverse.transform Table to a data frame.

Parameters:

target – Can currently be either a Polars or Pandas object. For polars, one can specify whether a DataFrame or LazyFrame is returned via the lazy keyword parameter. If lazy=True, no actual computations are performed, they just get stored in the LazyFrame.
schema_overrides – A dictionary of column names to backend-specific data types. This controls which data types are used when writing to the backend. Because the data types are not constrained by pydiverse.transform’s type system, this may sometimes be preferred over a cast.

Returns:

A polars or pandas DataFrame / LazyFrame.

Examples

>>> t1 = pdt.Table(
...     {
...         "a": [3, 1, 4, 1, 5, 9],
...         "b": [2.465, 0.22, -4.477, 10.8, -81.2, 0.0],
...         "c": ["a,", "bcbc", "pydiverse", "transform", "'  '", "-22"],
...     }
... )
>>> t1 >> export(Pandas())
   a      b          c
0  3  2.465         a,
1  1   0.22       bcbc
2  4 -4.477  pydiverse
3  1   10.8  transform
4  5  -81.2       '  '
5  9    0.0        -22
>>> t1 >> show()
shape: (6, 3)
┌─────┬────────┬───────────┐
│ a   ┆ b      ┆ c         │
│ --- ┆ ---    ┆ ---       │
│ i64 ┆ f64    ┆ str       │
╞═════╪════════╪═══════════╡
│ 3   ┆ 2.465  ┆ a,        │
│ 1   ┆ 0.22   ┆ bcbc      │
│ 4   ┆ -4.477 ┆ pydiverse │
│ 1   ┆ 10.8   ┆ transform │
│ 5   ┆ -81.2  ┆ '  '      │
│ 9   ┆ 0.0    ┆ -22       │
└─────┴────────┴───────────┘