0.6.1 (#84)

* 🐛 Fix `rep(df, n)` producing a nested df
🐛 Fix `TibbleGrouped.__getitem__()` not keeping grouping structures

* 🔖 0.6.1

datar/__init__.py

@@ -30,7 +30,7 @@
 )
 
 __all__ = ("f", "get_versions")
-__version__ = "0.6.0"
+__version__ = "0.6.1"
 
 
 def get_versions(prnt: bool = True) -> _VersionsTuple:

datar/base/arithmetic.py

@@ -90,7 +90,7 @@ def prod(x: "NDFrame", na_rm: bool = True) -> "NDFrame":
     (TibbleGrouped, GroupBy),
     "prod",
     pre=lambda x, na_rm=True: _warn_na_rm(
-        "sum", na_rm, "Use f.x.prod(min_count=...) to control NA produces."
+        "prod", na_rm, "Use f.x.prod(min_count=...) to control NA produces."
     )
     or (x, (), {}),
 )

datar/core/broadcast.py

@@ -684,11 +684,10 @@ def _(value: SeriesGroupBy, name: str) -> Tibble:
 @init_tibble_from.register(DataFrameGroupBy)
 def _(value: Union[DataFrame, DataFrameGroupBy], name: str) -> Tibble:
     from ..tibble import as_tibble
-
     result = regcall(as_tibble, value)
+
     if name:
-        if result is value:
-            result = value.copy()
+        result = result.copy()
         result.columns = [f"{name}${col}" for col in result.columns]
     return result
 

datar/core/factory.py

@@ -81,7 +81,7 @@ def _preprocess_args(sign: "Signature", data_args, args, kwargs):
             bound.arguments[arg] = args_df
         elif arg == "__args_raw":
             bound.arguments[arg] = args_raw
-        elif arg in args_df:
+        elif arg in args_df or args_df.columns.str.startswith(f"{arg}$").any():
             bound.arguments[arg] = args_df[arg]
         elif sign.parameters[arg].kind == sign.parameters[arg].VAR_POSITIONAL:
             star_args = bound.arguments[arg]
@@ -345,7 +345,6 @@ def func_factory(
 
     def _pipda_func(__x, *args, **kwargs):
         bound = _preprocess_args(sign, data_args, (__x, *args), kwargs)
-
         out = dispatched(*bound.args, **bound.kwargs)
         if (
             kind == "transform"

datar/core/tibble.py

@@ -12,7 +12,7 @@
 
 from .collections import Collection
 from .contexts import Context
-from .utils import name_of, regcall
+from .utils import apply_dtypes, name_of, regcall
 from .names import repair_names
 
 
@@ -86,19 +86,7 @@ def from_pairs(
         if _dtypes is True:
             return out.convert_dtypes()
 
-        if not isinstance(_dtypes, dict):
-            dtypes = zip(out.columns, [_dtypes] * out.shape[1])
-        else:
-            dtypes = _dtypes.items()
-
-        for column, dtype in dtypes:
-            if column in out:
-                out[column] = out[column].astype(dtype)
-            else:
-                for col in out:
-                    if col.startswith(f"{column}$"):
-                        out[col] = out[col].astype(dtype)
-
+        apply_dtypes(out, _dtypes)
         return out
 
     @classmethod
@@ -160,7 +148,7 @@ def __setitem__(self, key, value):
             else:
                 for col in value.columns:
                     colname = f"{key}${col}"
-                    super().__setitem__(colname, value[col])
+                    super().__setitem__(colname, value[col].copy())
 
         else:
             super().__setitem__(key, value)
@@ -268,7 +256,8 @@ def __getitem__(self, key):
         result = super().__getitem__(key)
         if isinstance(result, Series):
             return self._datar["grouped"][key]
-
+        if isinstance(result, DataFrame):
+            return TibbleGrouped(result, copy=False, meta=self._datar)
         return result  # pragma: no cover
 
     def __setitem__(self, key, value):
@@ -403,7 +392,8 @@ def __getitem__(self, key):
         result = super().__getitem__(key)
         if isinstance(result, SeriesGroupBy):
             result.is_rowwise = True
-
+        elif isinstance(result, DataFrame):
+            return reconstruct_tibble(self, result)
         return result
 
     def copy(self, deep: bool = True) -> "TibbleRowwise":

datar/dplyr/across.py

@@ -10,7 +10,7 @@
 from pipda.utils import functype
 
 from ..core.broadcast import add_to_tibble
-from ..core.tibble import Tibble
+from ..core.tibble import Tibble, reconstruct_tibble
 from ..core.utils import vars_select, regcall
 from ..core.middlewares import CurColumn
 from ..core.contexts import Context
@@ -226,7 +226,7 @@ def c_across(
         _cols = regcall(everything, _data)
 
     _cols = vars_select(_data.columns, _cols)
-    return _data.iloc[:, _cols]
+    return reconstruct_tibble(_data, _data.iloc[:, _cols])
 
 
 @register_func(

datar/tibble/tibble.py

@@ -163,7 +163,7 @@ def tibble_row(
 @register_verb(context=Context.EVAL)
 def as_tibble(df: Any) -> Tibble:
     """Convert a pandas DataFrame object to Tibble object"""
-    return Tibble(df)
+    return Tibble(df, copy=False)
 
 
 @as_tibble.register(DataFrameGroupBy)

docs/CHANGELOG.md

@@ -1,3 +1,8 @@
+## 0.6.1
+
+- 🐛 Fix `rep(df, n)` producing a nested df
+- 🐛 Fix `TibbleGrouped.__getitem__()` not keeping grouping structures
+
 ## 0.6.0
 
 ### General

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "datar"
-version = "0.6.0"
+version = "0.6.1"
 description = "Port of dplyr and other related R packages in python, using pipda."
 authors = ["pwwang <[email protected]>"]
 readme = "README.md"

tests/core/test_factory.py

@@ -47,7 +47,7 @@ def double(x):
     x = DataFrame({"a": [3, 4]})
     out = double(x)
     assert isinstance(out, DataFrame)
-    assert_iterable_equal(out["x$a"], [6, 8])
+    assert_iterable_equal(out.a, [6, 8])
 
     # default on seriesgroupby
     x = Series([1, 2, 1, 2]).groupby([1, 1, 2, 2])
@@ -60,7 +60,7 @@ def double(x):
     x = tibble(x=[1, 2, 1, 2], g=[1, 1, 2, 2]).group_by("g")
     out = double(x)
     # grouping variables not included
-    assert_iterable_equal(out.x, [2, 4, 2, 4])
+    assert_iterable_equal(out.x.obj, [2, 4, 2, 4])
 
     x = tibble(x=[1, 2, 1, 2], g=[1, 1, 2, 2]).rowwise("g")
     out = double(x)

tests/dplyr/test_mutate.py

@@ -145,11 +145,11 @@ def test_handles_data_frame_columns():
     assert_tibble_equal(res["new_col"], tibble(x=[1, 2, 3]))
 
     res = mutate(group_by(df, f.a), new_col=tibble(x=f.a))
-    assert_tibble_equal(res["new_col"], tibble(x=[1, 2, 3]))
+    assert_iterable_equal(res["new_col"].x.obj, [1, 2, 3])
 
     rf = rowwise(df, f.a)
     res = mutate(rf, new_col=tibble(x=f.a))
-    assert_tibble_equal(res["new_col"], tibble(x=[1, 2, 3]))
+    assert_tibble_equal(res["new_col"], tibble(x=[1, 2, 3]) >> rowwise())
 
 
 def test_unnamed_data_frames_are_automatically_unspliced():