REF: lreshape, wide_to_long (#55976)

* Refactor lreshape

* Refactor wide_to_long validation

* Refactor wide_to_long

* Annotation

pandas/core/reshape/melt.py

@@ -12,7 +12,6 @@
 from pandas.core.dtypes.missing import notna
 
 import pandas.core.algorithms as algos
-from pandas.core.arrays import Categorical
 from pandas.core.indexes.api import MultiIndex
 from pandas.core.reshape.concat import concat
 from pandas.core.reshape.util import tile_compat
@@ -139,7 +138,7 @@ def melt(
     return result
 
 
-def lreshape(data: DataFrame, groups, dropna: bool = True) -> DataFrame:
+def lreshape(data: DataFrame, groups: dict, dropna: bool = True) -> DataFrame:
     """
     Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.
 
@@ -192,30 +191,20 @@ def lreshape(data: DataFrame, groups, dropna: bool = True) -> DataFrame:
     2  Red Sox  2008  545
     3  Yankees  2008  526
     """
-    if isinstance(groups, dict):
-        keys = list(groups.keys())
-        values = list(groups.values())
-    else:
-        keys, values = zip(*groups)
-
-    all_cols = list(set.union(*(set(x) for x in values)))
-    id_cols = list(data.columns.difference(all_cols))
-
-    K = len(values[0])
-
-    for seq in values:
-        if len(seq) != K:
-            raise ValueError("All column lists must be same length")
-
     mdata = {}
     pivot_cols = []
-
-    for target, names in zip(keys, values):
+    all_cols: set[Hashable] = set()
+    K = len(next(iter(groups.values())))
+    for target, names in groups.items():
+        if len(names) != K:
+            raise ValueError("All column lists must be same length")
         to_concat = [data[col]._values for col in names]
 
         mdata[target] = concat_compat(to_concat)
         pivot_cols.append(target)
+        all_cols = all_cols.union(names)
 
+    id_cols = list(data.columns.difference(all_cols))
     for col in id_cols:
         mdata[col] = np.tile(data[col]._values, K)
 
@@ -467,10 +456,10 @@ def wide_to_long(
                 two  2.9
     """
 
-    def get_var_names(df, stub: str, sep: str, suffix: str) -> list[str]:
+    def get_var_names(df, stub: str, sep: str, suffix: str):
         regex = rf"^{re.escape(stub)}{re.escape(sep)}{suffix}$"
         pattern = re.compile(regex)
-        return [col for col in df.columns if pattern.match(col)]
+        return df.columns[df.columns.str.match(pattern)]
 
     def melt_stub(df, stub: str, i, j, value_vars, sep: str):
         newdf = melt(
@@ -480,7 +469,6 @@ def melt_stub(df, stub: str, i, j, value_vars, sep: str):
             value_name=stub.rstrip(sep),
             var_name=j,
         )
-        newdf[j] = Categorical(newdf[j])
         newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "", regex=True)
 
         # GH17627 Cast numerics suffixes to int/float
@@ -497,7 +485,7 @@ def melt_stub(df, stub: str, i, j, value_vars, sep: str):
     else:
         stubnames = list(stubnames)
 
-    if any(col in stubnames for col in df.columns):
+    if df.columns.isin(stubnames).any():
         raise ValueError("stubname can't be identical to a column name")
 
     if not is_list_like(i):
@@ -508,18 +496,18 @@ def melt_stub(df, stub: str, i, j, value_vars, sep: str):
     if df[i].duplicated().any():
         raise ValueError("the id variables need to uniquely identify each row")
 
-    value_vars = [get_var_names(df, stub, sep, suffix) for stub in stubnames]
-
-    value_vars_flattened = [e for sublist in value_vars for e in sublist]
-    id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))
+    _melted = []
+    value_vars_flattened = []
+    for stub in stubnames:
+        value_var = get_var_names(df, stub, sep, suffix)
+        value_vars_flattened.extend(value_var)
+        _melted.append(melt_stub(df, stub, i, j, value_var, sep))
 
-    _melted = [melt_stub(df, s, i, j, v, sep) for s, v in zip(stubnames, value_vars)]
-    melted = _melted[0].join(_melted[1:], how="outer")
+    melted = concat(_melted, axis=1)
+    id_vars = df.columns.difference(value_vars_flattened)
+    new = df[id_vars]
 
     if len(i) == 1:
-        new = df[id_vars].set_index(i).join(melted)
-        return new
-
-    new = df[id_vars].merge(melted.reset_index(), on=i).set_index(i + [j])
-
-    return new
+        return new.set_index(i).join(melted)
+    else:
+        return new.merge(melted.reset_index(), on=i).set_index(i + [j])