polars.DataFrame.update#

Update the values in this DataFrame with the values in other.

By default, null values in the right dataframe are ignored. Use ignore_nulls=False to overwrite values in this frame with null values in other frame.

Parameters:

other

DataFrame that will be used to update the values

on

Column names that will be joined on. If none given the row count is used.

left_on

Join column(s) of the left DataFrame.

right_on

Join column(s) of the right DataFrame.

how{‘left’, ‘inner’, ‘outer’}

‘left’ will keep all rows from the left table; rows may be duplicated if multiple rows in the right frame match the left row’s key.
‘inner’ keeps only those rows where the key exists in both frames.
‘outer’ will update existing rows where the key matches while also adding any new rows contained in the given frame.

include_nulls

If True, null values from the right dataframe will be used to update the left dataframe.

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

This is syntactic sugar for a left/inner join, with an optional coalesce when include_nulls = False.

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1, 2, 3, 4],
...         "B": [400, 500, 600, 700],
...     }
... )
>>> df
shape: (4, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 400 │
│ 2   ┆ 500 │
│ 3   ┆ 600 │
│ 4   ┆ 700 │
└─────┴─────┘
>>> new_df = pl.DataFrame(
...     {
...         "B": [-66, None, -99],
...         "C": [5, 3, 1],
...     }
... )

Update df values with the non-null values in new_df, by row index:

>>> df.update(new_df)
shape: (4, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ -66 │
│ 2   ┆ 500 │
│ 3   ┆ -99 │
│ 4   ┆ 700 │
└─────┴─────┘

Update df values with the non-null values in new_df, by row index, but only keeping those rows that are common to both frames:

>>> df.update(new_df, how="inner")
shape: (3, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ -66 │
│ 2   ┆ 500 │
│ 3   ┆ -99 │
└─────┴─────┘

Update df values with the non-null values in new_df, using an outer join strategy that defines explicit join columns in each frame:

>>> df.update(new_df, left_on=["A"], right_on=["C"], how="outer")
shape: (5, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ -99 │
│ 2   ┆ 500 │
│ 3   ┆ 600 │
│ 4   ┆ 700 │
│ 5   ┆ -66 │
└─────┴─────┘

Update df values including null values in new_df, using an outer join strategy that defines explicit join columns in each frame:

>>> df.update(
...     new_df, left_on="A", right_on="C", how="outer", include_nulls=True
... )
shape: (5, 2)
┌─────┬──────┐
│ A   ┆ B    │
│ --- ┆ ---  │
│ i64 ┆ i64  │
╞═════╪══════╡
│ 1   ┆ -99  │
│ 2   ┆ 500  │
│ 3   ┆ null │
│ 4   ┆ 700  │
│ 5   ┆ -66  │
└─────┴──────┘