narwhals.Expr.name

keep()

Keep the original root name of the 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
>>> 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:

>>> @nw.narwhalify
... def func(df):
...     return df.select(nw.col("foo").alias("alias_for_foo").name.keep())

We can then pass either pandas or Polars to func:

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

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
>>> 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
>>> @nw.narwhalify
... def func(df):
...     return df.select(nw.col("foo", "BAR").name.map(renaming_func))

We can then pass either pandas or Polars to func:

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

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
>>> 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:

>>> @nw.narwhalify
... def add_colname_prefix(df, prefix):
...     return df.select(nw.col("foo", "BAR").name.prefix(prefix))

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

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
>>> 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:

>>> @nw.narwhalify
... def add_colname_suffix(df, suffix):
...     return df.select(nw.col("foo", "BAR").name.suffix(suffix))

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.

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
>>> 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:

>>> @nw.narwhalify
... def to_lower(df):
...     return df.select(nw.col("foo", "BAR").name.to_lowercase())

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.

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
>>> 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:

>>> @nw.narwhalify
... def to_upper(df):
...     return df.select(nw.col("foo", "BAR").name.to_uppercase())

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']