narwhals.Expr.name

keep()

Keep the original root name of the expression.

Returns:

Type	Description
ExprT	A new expression.

Notes

This will undo any previous renaming operations on the expression. Due to implementation constraints, this method can only be called as the last expression in a chain. Only one name operation per expression will work.

Examples:

>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>> import pandas as pd
>>> import polars as pl
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)

We define a dataframe-agnostic function:

>>> def my_library_agnostic_function(df_native: IntoFrameT) -> IntoFrameT:
...     df = nw.from_native(df_native)
...     return df.select(
...         nw.col("foo").alias("alias_for_foo").name.keep()
...     ).to_native()

We can then pass either pandas or Polars to func:

>>> my_library_agnostic_function(df_pd).columns
Index(['foo'], dtype='object')
>>> my_library_agnostic_function(df_pl).columns
['foo']

map(function)

Rename the output of an expression by mapping a function over the root name.

Parameters:

Name	Type	Description	Default
function	Callable[[str], str]	Function that maps a root name to a new name.	required

Returns:

Type	Description
ExprT	A new expression.

Notes

This will undo any previous renaming operations on the expression. Due to implementation constraints, this method can only be called as the last expression in a chain. Only one name operation per expression will work.

Examples:

>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>> import pandas as pd
>>> import polars as pl
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)

We define a dataframe-agnostic function:

>>> renaming_func = lambda s: s[::-1]  # reverse column name
>>> def my_library_agnostic_function(df_native: IntoFrameT) -> IntoFrameT:
...     df = nw.from_native(df_native)
...     return df.select(nw.col("foo", "BAR").name.map(renaming_func)).to_native()

We can then pass either pandas or Polars to func:

>>> my_library_agnostic_function(df_pd).columns
Index(['oof', 'RAB'], dtype='object')
>>> my_library_agnostic_function(df_pl).columns
['oof', 'RAB']

prefix(prefix)

Add a prefix to the root column name of the expression.

Parameters:

Name	Type	Description	Default
prefix	str	Prefix to add to the root column name.	required

Returns:

Type	Description
ExprT	A new expression.

Notes

This will undo any previous renaming operations on the expression. Due to implementation constraints, this method can only be called as the last expression in a chain. Only one name operation per expression will work.

Examples:

>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>> import pandas as pd
>>> import polars as pl
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)

We define a dataframe-agnostic function:

>>> def add_colname_prefix(df_native: IntoFrameT, prefix: str) -> IntoFrameT:
...     df = nw.from_native(df_native)
...     return df.select(nw.col("foo", "BAR").name.prefix(prefix)).to_native()

We can then pass either pandas or Polars to func:

>>> add_colname_prefix(df_pd, "with_prefix_").columns
Index(['with_prefix_foo', 'with_prefix_BAR'], dtype='object')

>>> add_colname_prefix(df_pl, "with_prefix_").columns
['with_prefix_foo', 'with_prefix_BAR']

suffix(suffix)

Add a suffix to the root column name of the expression.

Parameters:

Name	Type	Description	Default
suffix	str	Suffix to add to the root column name.	required

Returns:

Type	Description
ExprT	A new expression.

Notes

This will undo any previous renaming operations on the expression. Due to implementation constraints, this method can only be called as the last expression in a chain. Only one name operation per expression will work.

Examples:

>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>> import pandas as pd
>>> import polars as pl
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)

We define a dataframe-agnostic function:

>>> def add_colname_suffix(df_native: IntoFrameT, suffix: str) -> IntoFrameT:
...     df = nw.from_native(df_native)
...     return df.select(nw.col("foo", "BAR").name.suffix(suffix)).to_native()

We can then pass either pandas or Polars to func:

>>> add_colname_suffix(df_pd, "_with_suffix").columns
Index(['foo_with_suffix', 'BAR_with_suffix'], dtype='object')
>>> add_colname_suffix(df_pl, "_with_suffix").columns
['foo_with_suffix', 'BAR_with_suffix']

to_lowercase()

Make the root column name lowercase.

Returns:

Type	Description
ExprT	A new expression.

Notes

This will undo any previous renaming operations on the expression. Due to implementation constraints, this method can only be called as the last expression in a chain. Only one name operation per expression will work.

Examples:

>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>> import pandas as pd
>>> import polars as pl
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)

We define a dataframe-agnostic function:

>>> def to_lower(df_native: IntoFrameT) -> IntoFrameT:
...     df = nw.from_native(df_native)
...     return df.select(nw.col("foo", "BAR").name.to_lowercase()).to_native()

We can then pass either pandas or Polars to func:

>>> to_lower(df_pd).columns
Index(['foo', 'bar'], dtype='object')
>>> to_lower(df_pl).columns
['foo', 'bar']

to_uppercase()

Make the root column name uppercase.

Returns:

Type	Description
ExprT	A new expression.

Notes

This will undo any previous renaming operations on the expression. Due to implementation constraints, this method can only be called as the last expression in a chain. Only one name operation per expression will work.

Examples:

>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>> import pandas as pd
>>> import polars as pl
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)

We define a dataframe-agnostic function:

>>> def to_upper(df_native: IntoFrameT) -> IntoFrameT:
...     df = nw.from_native(df_native)
...     return df.select(nw.col("foo", "BAR").name.to_uppercase()).to_native()

We can then pass either pandas or Polars to func:

>>> to_upper(df_pd).columns
Index(['FOO', 'BAR'], dtype='object')
>>> to_upper(df_pl).columns
['FOO', 'BAR']