update

py-polars/polars/convert.py

@@ -724,4 +724,6 @@ def from_pandas(
             include_index=include_index,
         )
     else:
-        raise ValueError(f"expected pandas DataFrame or Series, got {type(data).__name__!r}")
+        raise ValueError(
+            f"expected pandas DataFrame or Series, got {type(data).__name__!r}"
+        )

py-polars/polars/dataframe/frame.py

@@ -403,7 +403,7 @@ def __init__(
             )
         else:
             raise ValueError(
-                f"dataFrame constructor called with unsupported type; got {type(data).__name__!r}"
+                f"DataFrame constructor called with unsupported type; got {type(data).__name__!r}"
             )
 
     @classmethod
@@ -723,7 +723,7 @@ def _read_csv(
             if dtype_slice is not None:
                 raise ValueError(
                     "cannot use glob patterns and unnamed dtypes as `dtypes` argument;"
-                    "\n\nUse dtypes: Mapping[str, Type[DataType]"
+                    " Use dtypes: Mapping[str, Type[DataType]"
                 )
             from polars import scan_csv
 
@@ -755,7 +755,7 @@ def _read_csv(
             else:
                 raise ValueError(
                     "cannot use glob patterns and integer based projection as `columns`"
-                    "\n\nargument; Use columns: List[str]"
+                    " argument; Use columns: List[str]"
                 )
 
         projection, columns = handle_projection_columns(columns)
@@ -844,7 +844,7 @@ def _read_parquet(
             else:
                 raise ValueError(
                     "cannot use glob patterns and integer based projection as `columns`"
-                    "\n\nargument; Use columns: List[str]"
+                    " argument; Use columns: List[str]"
                 )
 
         projection, columns = handle_projection_columns(columns)
@@ -957,7 +957,7 @@ def _read_ipc(
             else:
                 raise ValueError(
                     "cannot use glob patterns and integer based projection as `columns`"
-                    "\n\nargument; Use columns: List[str]"
+                    " argument; Use columns: List[str]"
                 )
             return cls._from_pydf(df._df)
 
@@ -1247,7 +1247,7 @@ def __dataframe__(
         if nan_as_null:
             raise NotImplementedError(
                 "functionality for `nan_as_null` has not been implemented and the"
-                "\n\nparameter will be removed in a future version."
+                " parameter will be removed in a future version."
                 "\n\nUse the default `nan_as_null=False`"
             )
 
@@ -1546,7 +1546,7 @@ def __getitem__(
                     if len(col_selection) != self.width:
                         raise ValueError(
                             f"expected {self.width} values when selecting columns by"
-                            f"\n\nboolean mask. Got {len(col_selection)}"
+                            f" boolean mask. Got {len(col_selection)}"
                         )
                     series_list = []
                     for i, val in enumerate(col_selection):
@@ -1641,8 +1641,8 @@ def __getitem__(
 
         # if no data has been returned, the operation is not supported
         raise ValueError(
-            f"cannot __getitem__ on DataFrame with item: '{item!r}'"
-            f"\n\nof type: '{type(item).__name__!r}'"
+            f"cannot __getitem__ on DataFrame with item: {item!r}"
+            f" of type: {type(item).__name__!r}"
         )
 
     def __setitem__(
@@ -1654,7 +1654,7 @@ def __setitem__(
         if isinstance(key, str):
             raise TypeError(
                 "'DataFrame' object does not support 'Series' assignment by index."
-                "\n\nUse 'DataFrame.with_columns'"
+                " Use 'DataFrame.with_columns'"
             )
 
         # df[["C", "D"]]
@@ -1666,7 +1666,7 @@ def __setitem__(
             if value.shape[1] != len(key):
                 raise ValueError(
                     "matrix columns should be equal to list use to determine column"
-                    "\n\nnames"
+                    " names"
                 )
 
             # todo! we can parallelize this by calling from_numpy
@@ -1684,7 +1684,7 @@ def __setitem__(
             ) or is_bool_sequence(row_selection):
                 raise ValueError(
                     "not allowed to set 'DataFrame' by boolean mask in the"
-                    "\n\nrow position. Consider using 'DataFrame.with_columns'"
+                    " row position. Consider using 'DataFrame.with_columns'"
                 )
 
             # get series column selection
@@ -1707,9 +1707,9 @@ def __setitem__(
                 self.replace(col_selection, s)
         else:
             raise ValueError(
-                f"cannot __setitem__ on DataFrame with key: '{key!r}'"
-                f"\n\nof type: '{type(key).__name__!r}' and value: '{value!r}'"
-                f"\n\nof type: '{type(value).__name__!r}'."
+                f"cannot __setitem__ on DataFrame with key: {key!r}"
+                f" of type: {type(key).__name__!r} and value: {value!r}"
+                f" of type: {type(value).__name__!r}."
             )
 
     def __len__(self) -> int:
@@ -1787,7 +1787,7 @@ def item(self, row: int | None = None, column: int | str | None = None) -> Any:
             if self.shape != (1, 1):
                 raise ValueError(
                     f"can only call '.item()' if the dataframe is of shape (1,1), or if"
-                    f"\n\nexplicit row/col values are provided; frame has shape {self.shape!r}"
+                    f" explicit row/col values are provided; frame has shape {self.shape!r}"
                 )
             return self._df.select_at_idx(0).get_idx(0)
 
@@ -3288,7 +3288,7 @@ def write_database(
             else:
                 raise ValueError(
                     f"value for 'if_exists'={if_exists} was unexpected."
-                    f"\n\nChoose one of: {'fail', 'replace', 'append'}"
+                    f" Choose one of: {'fail', 'replace', 'append'}"
                 )
             with _open_adbc_connection(connection) as conn, conn.cursor() as cursor:
                 cursor.adbc_ingest(table_name, self.to_arrow(), mode)
@@ -7713,7 +7713,7 @@ def n_chunks(self, strategy: str = "first") -> int | list[int]:
         else:
             raise ValueError(
                 f"strategy: '{strategy}' not understood."
-                f"\n\nChoose one of {{'first',  'all'}}"
+                f" Choose one of {{'first',  'all'}}"
             )
 
     @overload
@@ -8666,7 +8666,7 @@ def row(
             if not isinstance(by_predicate, pl.Expr):
                 raise TypeError(
                     f"expected 'by_predicate to be an expression;"
-                    f"\n\nfound {type(by_predicate).__name__!r}"
+                    f" found {type(by_predicate).__name__!r}"
                 )
             rows = self.filter(by_predicate).rows()
             n_rows = len(rows)

py-polars/polars/datatypes/convert.py

@@ -431,7 +431,7 @@ def py_type_to_dtype(
         if not raise_unmatched:
             return None
         raise ValueError(
-            f"cannot infer dtype from '{data_type!r}' (type: {type(data_type).__name__!r})"
+            f"cannot infer dtype from {data_type!r} (type: {type(data_type).__name__!r})"
         ) from None
 
 

py-polars/polars/expr/expr.py

@@ -135,7 +135,7 @@ def __bool__(self) -> NoReturn:
         raise ValueError(
             "since Expr are lazy, the truthiness of an Expr is ambiguous."
             "\n\nHint: use '&' or '|' to logically combine Expr, not 'and'/'or', and"
-            "\n\nuse 'x.is_in([y,z])' instead of 'x in [y,z]' to check membership"
+            " use 'x.is_in([y,z])' instead of 'x in [y,z]' to check membership"
         )
 
     def __abs__(self) -> Self:
@@ -4932,7 +4932,7 @@ def is_between(
         else:
             raise ValueError(
                 "closed must be one of {'left', 'right', 'both', 'none'},"
-                f"\n\ngot {closed!r}"
+                f" got {closed!r}"
             )
 
     def hash(

py-polars/polars/functions/eager.py

@@ -177,7 +177,7 @@ def concat(
         else:
             raise ValueError(
                 f"`how` must be one of {{'vertical','vertical_relaxed','diagonal','horizontal','align'}},"
-                f"\n\ngot {how!r}"
+                f" ngot {how!r}"
             )
     elif isinstance(first, pl.LazyFrame):
         if how == "vertical":
@@ -194,12 +194,14 @@ def concat(
         if how == "vertical":
             out = wrap_s(plr.concat_series(elems))
         else:
-            raise ValueError("'Series' only allows 'vertical' concat strategy")
+            raise ValueError("'Series' only allows {'vertical'} concat strategy")
 
     elif isinstance(first, pl.Expr):
         return wrap_expr(plr.concat_expr([e._pyexpr for e in elems], rechunk))
     else:
-        raise ValueError(f"did not expect type: {type(first).__name__!r} in 'pl.concat'")
+        raise ValueError(
+            f"did not expect type: {type(first).__name__!r} in 'pl.concat'"
+        )
 
     if rechunk:
         return out.rechunk()

py-polars/polars/functions/lazy.py

@@ -203,7 +203,7 @@ def col(
         else:
             raise TypeError(
                 "invalid input for `col`. Expected iterable of type `str` or `DataType`,"
-                f"\n\ngot iterable of type {type(item).__name__!r}"
+                f" got iterable of type {type(item).__name__!r}"
             )
     else:
         raise TypeError(

py-polars/polars/functions/lit.py

@@ -89,7 +89,7 @@ def lit(
             and dtype.time_zone != str(value.tzinfo)  # type: ignore[union-attr]
         ):
             raise TypeError(
-                f"time zone of dtype ({dtype.time_zone!r}) differs from time zone of value ({value.tzinfo!r})."  # type: ignore[union-attr]
+                f"time zone of dtype ({dtype.time_zone!r}) differs from time zone of value ({value.tzinfo!r})"  # type: ignore[union-attr]
             )
         e = lit(_datetime_to_pl_timestamp(value, time_unit)).cast(Datetime(time_unit))
         if time_zone is not None:

py-polars/polars/interchange/column.py

@@ -35,7 +35,7 @@ def __init__(self, column: Series, *, allow_copy: bool = True):
             if not allow_copy:
                 raise RuntimeError(
                     f"column {column.name!r} must be converted to a local categorical,"
-                    "\n\nwhich is not zero-copy"
+                    " which is not zero-copy"
                 )
             column = column.cat.to_local()
 

py-polars/polars/interchange/from_dataframe.py

@@ -74,7 +74,7 @@ def _df_to_pyarrow_table(df: Any, *, allow_copy: bool = False) -> pa.Table:
     if not _PYARROW_AVAILABLE or parse_version(pa.__version__) < parse_version("11"):
         raise ImportError(
             "pyarrow>=11.0.0 is required for converting a dataframe interchange object"
-            "\n\nto a Polars dataframe"
+            " to a Polars dataframe"
         )
 
     import pyarrow.interchange  # noqa: F401
@@ -90,8 +90,8 @@ def _df_to_pyarrow_table_zero_copy(df: Any) -> pa.Table:
     if _dfi_contains_categorical_data(dfi):
         raise TypeError(
             "Polars can not currently guarantee zero-copy conversion from Arrow for"
-            "\n\ncategorical columns. Set `allow_copy=True` or cast categorical columns to"
-            "\n\nstring first"
+            " categorical columns. Set `allow_copy=True` or cast categorical columns to"
+            " string first"
         )
 
     if isinstance(df, pa.Table):

py-polars/polars/io/csv/_utils.py

@@ -15,7 +15,7 @@ def _check_arg_is_1byte(
             if arg_byte_length > 1:
                 raise ValueError(
                     f'{arg_name}="{arg}" should be a single byte character or empty,'
-                    f"\n\nbut is {arg_byte_length} bytes long"
+                    f" but is {arg_byte_length} bytes long"
                 )
         elif arg_byte_length != 1:
             raise ValueError(

py-polars/polars/io/csv/functions.py

@@ -195,7 +195,7 @@ def read_csv(
             if not column.startswith("column_"):
                 raise ValueError(
                     'specified column names do not start with "column_",'
-                    "\n\nbut autogenerated header names were requested"
+                    " but autogenerated header names were requested"
                 )
 
     if (
@@ -302,8 +302,8 @@ def read_csv(
         if columns:
             if len(columns) < len(new_columns):
                 raise ValueError(
-                    "More new column names are specified than there are selected"
-                    "\n\ncolumns"
+                    "more new column names are specified than there are selected"
+                    " columns"
                 )
 
             # Get column names of requested columns.
@@ -315,7 +315,7 @@ def read_csv(
                 if columns and len(columns) < len(new_columns):
                     raise ValueError(
                         "more new column names are specified than there are selected"
-                        "\n\ncolumns"
+                        " columns"
                     )
                 # Convert column indices from projection to 'column_1', 'column_2', ...
                 # column names.
@@ -567,7 +567,7 @@ def read_csv_batched(
             if not column.startswith("column_"):
                 raise ValueError(
                     'specified column names do not start with "column_",'
-                    "\n\nbut autogenerated header names were requested."
+                    " but autogenerated header names were requested."
                 )
 
     if projection and dtypes and isinstance(dtypes, list):
@@ -607,7 +607,7 @@ def read_csv_batched(
             if len(columns) < len(new_columns):
                 raise ValueError(
                     "more new column names are specified than there are selected"
-                    "\n\ncolumns"
+                    " columns"
                 )
 
             # Get column names of requested columns.
@@ -619,7 +619,7 @@ def read_csv_batched(
                 if columns and len(columns) < len(new_columns):
                     raise ValueError(
                         "more new column names are specified than there are selected"
-                        "\n\ncolumns"
+                        " columns"
                     )
                 # Convert column indices from projection to 'column_1', 'column_2', ...
                 # column names.

py-polars/polars/io/database.py

@@ -184,7 +184,7 @@ def _open_adbc_connection(connection_uri: str) -> Any:
     except ImportError:
         raise ImportError(
             f"ADBC {driver_name} driver not detected; if ADBC supports this database,"
-            f"\n\nplease run `pip install adbc-driver-{driver_name} pyarrow`"
+            f" please run `pip install adbc-driver-{driver_name} pyarrow`"
         ) from None
 
     # some backends require the driver name to be stripped from the URI

py-polars/polars/io/ipc/functions.py

@@ -78,7 +78,7 @@ def read_ipc(
     if use_pyarrow and n_rows and not memory_map:
         raise ValueError(
             "``n_rows`` cannot be used with ``use_pyarrow=True`"
-            "\n\nand memory_map=False`"
+            " and memory_map=False`"
         )
 
     storage_options = storage_options or {}

py-polars/polars/lazyframe/frame.py

@@ -744,11 +744,13 @@ def width(self) -> int:
     def __bool__(self) -> NoReturn:
         raise ValueError(
             "the truth value of a LazyFrame is ambiguous; consequently it"
-            "\n\ncannot be used in boolean context with and/or/not operators"
+            " cannot be used in boolean context with and/or/not operators"
         )
 
     def _comparison_error(self, operator: str) -> NoReturn:
-        raise TypeError(f'"{operator!r}" comparison not supported for LazyFrame objects')
+        raise TypeError(
+            f'"{operator!r}" comparison not supported for LazyFrame objects'
+        )
 
     def __eq__(self, other: Any) -> NoReturn:
         self._comparison_error("==")

py-polars/polars/lazyframe/groupby.py

@@ -137,7 +137,7 @@ def agg(
             raise ValueError(
                 "specifying aggregations as a dictionary is not supported."
                 "\n\nTry unpacking the dictionary to take advantage of the keyword syntax"
-                "\n\nof the `agg` method"
+                " of the `agg` method"
             )
 
         if "aggs" in named_aggs: