Review (WillAyd)

pandas/core/strings.py

@@ -37,15 +37,15 @@
 _shared_docs = dict()
 
 
-def cat_core(list_of_columns, sep):
+def cat_core(all_cols, sep):
     """
     Auxiliary function for :meth:`str.cat`
 
     Parameters
     ----------
-    list_of_columns : list of numpy arrays
-        List of arrays to be concatenated with sep;
-        these arrays may not contain NaNs!
+    all_cols : two-dimensional numpy array
+        array of columns to be concatenated with sep;
+        this array may not contain NaNs!
     sep : string
         The separator string for concatenating the columns
 
@@ -54,9 +54,12 @@ def cat_core(list_of_columns, sep):
     nd.array
         The concatenation of list_of_columns with sep
     """
+    list_of_columns = np.split(all_cols, all_cols.shape[1], axis=1)
     list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
     list_with_sep[::2] = list_of_columns
-    return np.sum(list_with_sep, axis=0)
+    # np.split splits into arrays of shape (N, 1); NOT (N,)
+    # need to reduce dimensionality of result
+    return np.sum(list_with_sep, axis=0)[:, 0]
 
 
 def _na_map(f, arr, na_result=np.nan, dtype=object):
@@ -2239,21 +2242,21 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
                           "'outer'|'inner'|'right'`. The future default will "
                           "be `join='left'`.", FutureWarning, stacklevel=2)
 
-        # if join is None, _get_series_list already aligned indexes
-        join = 'left' if join is None else join
+        # concatenate others into DataFrame; need to add keys for uniqueness in
+        # case of duplicate columns (for join is None, all indexes are already
+        # the same after _get_series_list, which forces alignment in this case)
+        others = concat(others, axis=1,
+                        join=(join if join == 'inner' else 'outer'),
+                        keys=range(len(others)), copy=False)
 
         # align if required
-        if any(not data.index.equals(x.index) for x in others):
-            # Need to add keys for uniqueness in case of duplicate columns
-            others = concat(others, axis=1,
-                            join=(join if join == 'inner' else 'outer'),
-                            keys=range(len(others)), copy=False)
+        if not data.index.equals(others.index):
             data, others = data.align(others, join=join)
-            others = [others[x] for x in others]  # again list of Series
 
-        all_cols = [ensure_object(x) for x in [data] + others]
-        na_masks = np.array([isna(x) for x in all_cols])
-        union_mask = np.logical_or.reduce(na_masks, axis=0)
+        # collect all columns
+        all_cols = ensure_object(concat([data, others], axis=1, copy=False))
+        na_masks = isna(all_cols)
+        union_mask = np.logical_or.reduce(na_masks, axis=1)
 
         if na_rep is None and union_mask.any():
             # no na_rep means NaNs for all rows where any column has a NaN
@@ -2262,13 +2265,10 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
             np.putmask(result, union_mask, np.nan)
 
             not_masked = ~union_mask
-            result[not_masked] = cat_core([x[not_masked] for x in all_cols],
-                                          sep)
+            result[not_masked] = cat_core(all_cols[not_masked], sep)
         elif na_rep is not None and union_mask.any():
             # fill NaNs with na_rep in case there are actually any NaNs
-            all_cols = [np.where(nm, na_rep, col)
-                        for nm, col in zip(na_masks, all_cols)]
-            result = cat_core(all_cols, sep)
+            result = cat_core(np.where(na_masks, na_rep, all_cols), sep)
         else:
             # no NaNs - can just concatenate
             result = cat_core(all_cols, sep)