CLN/ERR: str.cat internals

pandas/core/strings.py

@@ -3,8 +3,9 @@
 
 from pandas.compat import zip
 from pandas.core.dtypes.generic import ABCSeries, ABCIndex
-from pandas.core.dtypes.missing import isna, notna
+from pandas.core.dtypes.missing import isna
 from pandas.core.dtypes.common import (
+    ensure_object,
     is_bool_dtype,
     is_categorical_dtype,
     is_object_dtype,
@@ -36,114 +37,26 @@
 _shared_docs = dict()
 
 
-def _get_array_list(arr, others):
-    """
-    Auxiliary function for :func:`str_cat`
-
-    Parameters
-    ----------
-    arr : ndarray
-        The left-most ndarray of the concatenation
-    others : list, ndarray, Series
-        The rest of the content to concatenate. If list of list-likes,
-        all elements must be passable to ``np.asarray``.
-
-    Returns
-    -------
-    list
-        List of all necessary arrays
-    """
-    from pandas.core.series import Series
-
-    if len(others) and isinstance(com.values_from_object(others)[0],
-                                  (list, np.ndarray, Series)):
-        arrays = [arr] + list(others)
-    else:
-        arrays = [arr, others]
-
-    return [np.asarray(x, dtype=object) for x in arrays]
-
-
-def str_cat(arr, others=None, sep=None, na_rep=None):
+def cat_core(list_of_columns, sep):
     """
     Auxiliary function for :meth:`str.cat`
 
-    If `others` is specified, this function concatenates the Series/Index
-    and elements of `others` element-wise.
-    If `others` is not being passed then all values in the Series are
-    concatenated in a single string with a given `sep`.
-
     Parameters
     ----------
-    others : list-like, or list of list-likes, optional
-        List-likes (or a list of them) of the same length as calling object.
-        If None, returns str concatenating strings of the Series.
-    sep : string or None, default None
-        If None, concatenates without any separator.
-    na_rep : string or None, default None
-        If None, NA in the series are ignored.
+    list_of_columns : list of numpy arrays
+        List of arrays to be concatenated with sep;
+        these arrays may not contain NaNs!
+    sep : string
+        The separator string for concatenating the columns
 
     Returns
     -------
-    concat
-        ndarray containing concatenated results (if `others is not None`)
-        or str (if `others is None`)
+    nd.array
+        The concatenation of list_of_columns with sep
     """
-    if sep is None:
-        sep = ''
-
-    if others is not None:
-        arrays = _get_array_list(arr, others)
-
-        n = _length_check(arrays)
-        masks = np.array([isna(x) for x in arrays])
-        cats = None
-
-        if na_rep is None:
-            na_mask = np.logical_or.reduce(masks, axis=0)
-
-            result = np.empty(n, dtype=object)
-            np.putmask(result, na_mask, np.nan)
-
-            notmask = ~na_mask
-
-            tuples = zip(*[x[notmask] for x in arrays])
-            cats = [sep.join(tup) for tup in tuples]
-
-            result[notmask] = cats
-        else:
-            for i, x in enumerate(arrays):
-                x = np.where(masks[i], na_rep, x)
-                if cats is None:
-                    cats = x
-                else:
-                    cats = cats + sep + x
-
-            result = cats
-
-        return result
-    else:
-        arr = np.asarray(arr, dtype=object)
-        mask = isna(arr)
-        if na_rep is None and mask.any():
-            if sep == '':
-                na_rep = ''
-            else:
-                return sep.join(arr[notna(arr)])
-        return sep.join(np.where(mask, na_rep, arr))
-
-
-def _length_check(others):
-    n = None
-    for x in others:
-        try:
-            if n is None:
-                n = len(x)
-            elif len(x) != n:
-                raise ValueError('All arrays must be same length')
-        except TypeError:
-            raise ValueError('Must pass arrays containing strings to str_cat')
-    return n
+    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)
 
 
 def _na_map(f, arr, na_result=np.nan, dtype=object):
@@ -2283,6 +2196,8 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
 
         if isinstance(others, compat.string_types):
             raise ValueError("Did you mean to supply a `sep` keyword?")
+        if sep is None:
+            sep = ''
 
         if isinstance(self._orig, Index):
             data = Series(self._orig, index=self._orig)
@@ -2291,9 +2206,13 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
 
         # concatenate Series/Index with itself if no "others"
         if others is None:
-            result = str_cat(data, others=others, sep=sep, na_rep=na_rep)
-            return self._wrap_result(result,
-                                     use_codes=(not self._is_categorical))
+            data = ensure_object(data)
+            na_mask = isna(data)
+            if na_rep is None and na_mask.any():
+                data = data[~na_mask]
+            elif na_rep is not None and na_mask.any():
+                data = np.where(na_mask, na_rep, data)
+            return sep.join(data)
 
         try:
             # turn anything in "others" into lists of Series
@@ -2320,23 +2239,45 @@ 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 force-aligned indexes
+        join = 'left' if join is None else join
+
         # align if required
-        if join is not None:
+        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)))
+                            keys=range(len(others)), copy=False)
             data, others = data.align(others, join=join)
             others = [others[x] for x in others]  # again list of Series
 
-        # str_cat discards index
-        res = str_cat(data, others=others, sep=sep, na_rep=na_rep)
+        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)
+
+        if na_rep is None and union_mask.any():
+            # no na_rep means NaNs for all rows where any column has a NaN
+            # only necessary if there are actually any NaNs
+            result = np.empty(len(data), dtype=object)
+            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)
+        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)
+        else:
+            # no NaNs - can just concatenate
+            result = cat_core(all_cols, sep)
 
         if isinstance(self._orig, Index):
-            res = Index(res, name=self._orig.name)
+            result = Index(result, name=self._orig.name)
         else:  # Series
-            res = Series(res, index=data.index, name=self._orig.name)
-        return res
+            result = Series(result, index=data.index, name=self._orig.name)
+        return result
 
     _shared_docs['str_split'] = ("""
     Split strings around given separator/delimiter.

pandas/tests/test_strings.py

@@ -97,53 +97,6 @@ def test_iter_object_try_string(self):
         assert i == 100
         assert s == 'h'
 
-    def test_cat(self):
-        one = np.array(['a', 'a', 'b', 'b', 'c', NA], dtype=np.object_)
-        two = np.array(['a', NA, 'b', 'd', 'foo', NA], dtype=np.object_)
-
-        # single array
-        result = strings.str_cat(one)
-        exp = 'aabbc'
-        assert result == exp
-
-        result = strings.str_cat(one, na_rep='NA')
-        exp = 'aabbcNA'
-        assert result == exp
-
-        result = strings.str_cat(one, na_rep='-')
-        exp = 'aabbc-'
-        assert result == exp
-
-        result = strings.str_cat(one, sep='_', na_rep='NA')
-        exp = 'a_a_b_b_c_NA'
-        assert result == exp
-
-        result = strings.str_cat(two, sep='-')
-        exp = 'a-b-d-foo'
-        assert result == exp
-
-        # Multiple arrays
-        result = strings.str_cat(one, [two], na_rep='NA')
-        exp = np.array(['aa', 'aNA', 'bb', 'bd', 'cfoo', 'NANA'],
-                       dtype=np.object_)
-        tm.assert_numpy_array_equal(result, exp)
-
-        result = strings.str_cat(one, two)
-        exp = np.array(['aa', NA, 'bb', 'bd', 'cfoo', NA], dtype=np.object_)
-        tm.assert_almost_equal(result, exp)
-
-        # error for incorrect lengths
-        rgx = 'All arrays must be same length'
-        three = Series(['1', '2', '3'])
-
-        with tm.assert_raises_regex(ValueError, rgx):
-            strings.str_cat(one, three)
-
-        # error for incorrect type
-        rgx = "Must pass arrays containing strings to str_cat"
-        with tm.assert_raises_regex(ValueError, rgx):
-            strings.str_cat(one, 'three')
-
     @pytest.mark.parametrize('box', [Series, Index])
     @pytest.mark.parametrize('other', [None, Series, Index])
     def test_str_cat_name(self, box, other):
@@ -414,6 +367,12 @@ def test_str_cat_align_mixed_inputs(self, join):
         with tm.assert_raises_regex(ValueError, rgx):
             s.str.cat([t, z], join=join)
 
+    def test_str_cat_raises(self):
+        # non-strings hiding behind object dtype
+        s = Series([1, 2, 3, 4], dtype='object')
+        with tm.assert_raises_regex(TypeError, "unsupported operand type.*"):
+            s.str.cat(s)
+
     def test_str_cat_special_cases(self):
         s = Series(['a', 'b', 'c', 'd'])
         t = Series(['d', 'a', 'e', 'b'], index=[3, 0, 4, 1])