Move pipe() from datar/apis/dplyr.py to datar/misc.py

Co-authored-by: pwwang <[email protected]>

Author: Copilot

datar/apis/dplyr.py

@@ -2478,43 +2478,3 @@ def any_of(_data, x, vars=None) -> Any:
             in `_data` columns
     """
     raise _NotImplementedByCurrentBackendError("any_of", _data)
-
-
-@_register_verb(object)
-def pipe(_data: T, func: _Callable, *args, **kwargs) -> Any:
-    """Apply a function to the data
-
-    This function is similar to pandas.DataFrame.pipe() and allows you to
-    apply custom functions in a piping workflow. Works with any data type.
-
-    Args:
-        _data: The data object (can be any type)
-        func: Function to apply to the data. ``args`` and ``kwargs`` are
-            passed into ``func``.
-        *args: Positional arguments passed into ``func``
-        **kwargs: Keyword arguments passed into ``func``
-
-    Returns:
-        The return value of ``func``
-
-    Examples:
-        >>> import datar.all as dr
-        >>> # Works with lists
-        >>> [1, 2, 3] >> dr.pipe(lambda x: [i * 2 for i in x])
-        [2, 4, 6]
-
-        >>> # Works with dicts
-        >>> {'a': 1, 'b': 2} >> dr.pipe(lambda x: {k: v * 2 for k, v in x.items()})
-        {'a': 2, 'b': 4}
-
-        >>> # With additional arguments
-        >>> def add_value(data, value):
-        ...     return [x + value for x in data]
-        >>> [1, 2, 3] >> dr.pipe(add_value, 10)
-        [11, 12, 13]
-
-        >>> # Chain multiple operations
-        >>> [1, 2, 3] >> dr.pipe(lambda x: [i * 2 for i in x]) >> dr.pipe(sum)
-        12
-    """
-    return func(_data, *args, **kwargs)

datar/misc.py

@@ -1,3 +1,47 @@
+from typing import Any, Callable
+
+from pipda import register_verb
+
 from .core.load_plugins import plugin as _plugin
 
 locals().update(_plugin.hooks.misc_api())
+
+
+@register_verb(object)
+def pipe(data: Any, func: Callable, *args, **kwargs) -> Any:
+    """Apply a function to the data
+
+    This function is similar to pandas.DataFrame.pipe() and allows you to
+    apply custom functions in a piping workflow. Works with any data type.
+
+    Args:
+        data: The data object (can be any type)
+        func: Function to apply to the data. ``args`` and ``kwargs`` are
+            passed into ``func``.
+        *args: Positional arguments passed into ``func``
+        **kwargs: Keyword arguments passed into ``func``
+
+    Returns:
+        The return value of ``func``
+
+    Examples:
+        >>> import datar.all as dr
+        >>> # Works with lists
+        >>> [1, 2, 3] >> dr.pipe(lambda x: [i * 2 for i in x])
+        [2, 4, 6]
+
+        >>> # Works with dicts
+        >>> {'a': 1, 'b': 2} >> dr.pipe(lambda x: {k: v * 2 for k, v in x.items()})
+        {'a': 2, 'b': 4}
+
+        >>> # With additional arguments
+        >>> def add_value(data, value):
+        ...     return [x + value for x in data]
+        >>> [1, 2, 3] >> dr.pipe(add_value, 10)
+        [11, 12, 13]
+
+        >>> # Chain multiple operations
+        >>> [1, 2, 3] >> dr.pipe(lambda x: [i * 2 for i in x]) >> dr.pipe(sum)
+        12
+    """
+    return func(data, *args, **kwargs)