Remove typing as tp import (#2101)

This was done to resolve conflicts between typing.List and polars.datatypes.List. Given that we rarely use the latter in the code, but mostly typing.List, making this change. Makes the code more pythonic, especially as in newer python versions we could even do `list[int]` rather than `List[int]`.

py-polars/polars/internals/expr.py

@@ -1,7 +1,6 @@
 import copy
-import typing as tp
 from datetime import date, datetime, timedelta
-from typing import Any, Callable, Optional, Sequence, Type, Union
+from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union
 
 import numpy as np
 
@@ -29,8 +28,8 @@
 
 def _selection_to_pyexpr_list(
     exprs: Union[str, "Expr", Sequence[Union[str, "Expr"]], "pli.Series"]
-) -> tp.List["PyExpr"]:
-    pyexpr_list: tp.List[PyExpr]
+) -> List["PyExpr"]:
+    pyexpr_list: List[PyExpr]
     if isinstance(exprs, Sequence) and not isinstance(exprs, str):
         pyexpr_list = []
         for expr in exprs:
@@ -287,7 +286,7 @@ def alias(self, name: str) -> "Expr":
 
     def exclude(
         self,
-        columns: Union[str, tp.List[str], Type[DataType], Sequence[Type[DataType]]],
+        columns: Union[str, List[str], Type[DataType], Sequence[Type[DataType]]],
     ) -> "Expr":
         """
         Exclude certain columns from a wildcard/regex selection.
@@ -806,8 +805,8 @@ def arg_min(self) -> "Expr":
 
     def sort_by(
         self,
-        by: Union["Expr", str, tp.List[Union["Expr", str]]],
-        reverse: Union[bool, tp.List[bool]] = False,
+        by: Union["Expr", str, List[Union["Expr", str]]],
+        reverse: Union[bool, List[bool]] = False,
     ) -> "Expr":
         """
         Sort this column by the ordering of another column, or multiple other columns.
@@ -830,9 +829,7 @@ def sort_by(
 
         return wrap_expr(self._pyexpr.sort_by(by, reverse))
 
-    def take(
-        self, index: Union[tp.List[int], "Expr", "pli.Series", np.ndarray]
-    ) -> "Expr":
+    def take(self, index: Union[List[int], "Expr", "pli.Series", np.ndarray]) -> "Expr":
         """
         Take values by index.
 
@@ -1014,7 +1011,7 @@ def list(self) -> "Expr":
         """
         return wrap_expr(self._pyexpr.list())
 
-    def over(self, expr: Union[str, "Expr", tp.List[Union["Expr", str]]]) -> "Expr":
+    def over(self, expr: Union[str, "Expr", List[Union["Expr", str]]]) -> "Expr":
         """
         Apply window function over a subgroup.
         This is similar to a groupby + aggregation + self join.
@@ -1274,7 +1271,7 @@ def pow(self, exponent: float) -> "Expr":
         """
         return wrap_expr(self._pyexpr.pow(exponent))
 
-    def is_in(self, other: Union["Expr", tp.List[Any]]) -> "Expr":
+    def is_in(self, other: Union["Expr", List[Any]]) -> "Expr":
         """
         Check if elements of this Series are in the right Series, or List values of the right Series.
 
@@ -1426,7 +1423,7 @@ def interpolate(self) -> "Expr":
     def rolling_min(
         self,
         window_size: int,
-        weights: Optional[tp.List[float]] = None,
+        weights: Optional[List[float]] = None,
         min_periods: Optional[int] = None,
         center: bool = False,
     ) -> "Expr":
@@ -1458,7 +1455,7 @@ def rolling_min(
     def rolling_max(
         self,
         window_size: int,
-        weights: Optional[tp.List[float]] = None,
+        weights: Optional[List[float]] = None,
         min_periods: Optional[int] = None,
         center: bool = False,
     ) -> "Expr":
@@ -1490,7 +1487,7 @@ def rolling_max(
     def rolling_mean(
         self,
         window_size: int,
-        weights: Optional[tp.List[float]] = None,
+        weights: Optional[List[float]] = None,
         min_periods: Optional[int] = None,
         center: bool = False,
     ) -> "Expr":
@@ -1551,7 +1548,7 @@ def rolling_mean(
     def rolling_sum(
         self,
         window_size: int,
-        weights: Optional[tp.List[float]] = None,
+        weights: Optional[List[float]] = None,
         min_periods: Optional[int] = None,
         center: bool = False,
     ) -> "Expr":
@@ -1583,7 +1580,7 @@ def rolling_sum(
     def rolling_std(
         self,
         window_size: int,
-        weights: Optional[tp.List[float]] = None,
+        weights: Optional[List[float]] = None,
         min_periods: Optional[int] = None,
         center: bool = False,
     ) -> "Expr":
@@ -1616,7 +1613,7 @@ def rolling_std(
     def rolling_var(
         self,
         window_size: int,
-        weights: Optional[tp.List[float]] = None,
+        weights: Optional[List[float]] = None,
         min_periods: Optional[int] = None,
         center: bool = False,
     ) -> "Expr":
@@ -2068,7 +2065,7 @@ def arctan(self) -> "Expr":
         """
         return np.arctan(self)  # type: ignore
 
-    def reshape(self, dims: tp.Tuple[int, ...]) -> "Expr":
+    def reshape(self, dims: Tuple[int, ...]) -> "Expr":
         """
         Reshape this Expr to a flat series, shape: (len,)
         or a List series, shape: (rows, cols)
@@ -2178,7 +2175,7 @@ def unique(self) -> "Expr":
         """
         return wrap_expr(self._pyexpr.lst_unique())
 
-    def concat(self, other: Union[tp.List[Union[Expr, str]], Expr, str]) -> "Expr":
+    def concat(self, other: Union[List[Union[Expr, str]], Expr, str]) -> "Expr":
         """
         Concat the arrays in a Series dtype List in linear time.
 
@@ -2187,7 +2184,7 @@ def concat(self, other: Union[tp.List[Union[Expr, str]], Expr, str]) -> "Expr":
         other
             Columns to concat into a List Series
         """
-        other_list: tp.List[Union[Expr, str]]
+        other_list: List[Union[Expr, str]]
         if not isinstance(other, list):
             other_list = [other]
         else:
@@ -2748,7 +2745,7 @@ def timestamp(self) -> Expr:
 
 
 def expr_to_lit_or_expr(
-    expr: Union[Expr, bool, int, float, str, tp.List[Expr], tp.List[str], "pli.Series"],
+    expr: Union[Expr, bool, int, float, str, List[Expr], List[str], "pli.Series"],
     str_to_lit: bool = True,
 ) -> Expr:
     """