second version of code

datascience/tables.py

@@ -550,7 +550,7 @@ def apply(self, fn, *column_or_columns):
             return np.array([fn(row) for row in self.rows])
         else:
             if len(column_or_columns) == 1 and \
-                    _is_non_string_iterable(column_or_columns[0]):
+                    _util.is_non_string_iterable(column_or_columns[0]):
                 warnings.warn(
                    "column lists are deprecated; pass each as an argument", FutureWarning)
                 column_or_columns = column_or_columns[0]
@@ -998,9 +998,14 @@ def append_column(self, label, values, formatter=None):
 
         if not isinstance(values, np.ndarray):
             # Coerce a single value to a sequence
-            if not _is_non_string_iterable(values):
+            if not _util.is_non_string_iterable(values):
                 values = [values] * max(self.num_rows, 1)
-            values = np.array(tuple(values))
+
+            # Manually cast `values` as an object due to this: https://github.com/data-8/datascience/issues/458
+            if any(_util.is_non_string_iterable(el) for el in values):
+                values = np.array(tuple(values), dtype=object)
+            else:
+                values = np.array(tuple(values))
 
         if self.num_rows != 0 and len(values) != self.num_rows:
             raise ValueError('Column length mismatch. New column does not have '
@@ -1563,7 +1568,7 @@ def group(self, column_or_label, collect=None):
         Round       |           | 13         | 4.05
         """
         # Assume that a call to group with a list of labels is a call to groups
-        if _is_non_string_iterable(column_or_label) and \
+        if _util.is_non_string_iterable(column_or_label) and \
                 len(column_or_label) != self._num_rows:
             return self.groups(column_or_label, collect)
 
@@ -1647,7 +1652,7 @@ def groups(self, labels, collect=None):
         Red   | Round       | 11         | 3.05
         """
         # Assume that a call to groups with one label is a call to group
-        if not _is_non_string_iterable(labels):
+        if not _util.is_non_string_iterable(labels):
             return self.group(labels, collect=collect)
 
         collect = _zero_on_type_error(collect)
@@ -2042,7 +2047,7 @@ def join(self, column_label, other, other_label=None):
             other_label = column_label
 
         # checking to see if joining on multiple columns
-        if _is_non_string_iterable(column_label):
+        if _util.is_non_string_iterable(column_label):
             # then we are going to be joining multiple labels
             return self._multiple_join(column_label, other, other_label)
 
@@ -5734,7 +5739,7 @@ def _fill_with_zeros(partials, rows, zero=None):
     zero -- value used when no rows match a particular partial
     """
     assert len(rows) > 0
-    if not _is_non_string_iterable(partials):
+    if not _util.is_non_string_iterable(partials):
         # Convert partials to tuple for comparison against row slice later
         partials = [(partial,) for partial in partials]
 
@@ -5753,7 +5758,7 @@ def _fill_with_zeros(partials, rows, zero=None):
 
 def _as_labels(column_or_columns):
     """Return a list of labels for a label or labels."""
-    if not _is_non_string_iterable(column_or_columns):
+    if not _util.is_non_string_iterable(column_or_columns):
         return [column_or_columns]
     else:
         return column_or_columns
@@ -5763,7 +5768,7 @@ def _varargs_labels_as_list(label_list):
     of labels."""
     if len(label_list) == 0:
         return []
-    elif not _is_non_string_iterable(label_list[0]):
+    elif not _util.is_non_string_iterable(label_list[0]):
         # Assume everything is a label.  If not, it'll be caught later.
         return label_list
     elif len(label_list) == 1:
@@ -5788,17 +5793,6 @@ def _collected_label(collect, label):
     else:
         return label
 
-
-def _is_non_string_iterable(value):
-    """Whether a value is iterable."""
-    if isinstance(value, str):
-        return False
-    if hasattr(value, '__iter__'):
-        return True
-    if isinstance(value, collections.abc.Sequence):
-        return True
-    return False
-
 def _vertical_x(axis, ticks=None, max_width=5):
     """Switch labels to vertical if they are long."""
     if ticks is None:

datascience/util.py

@@ -2,7 +2,7 @@
 
 __all__ = ['make_array', 'percentile', 'plot_cdf_area', 'plot_normal_cdf',
            'table_apply', 'proportions_from_distribution',
-           'sample_proportions', 'minimize']
+           'sample_proportions', 'minimize', 'is_non_string_iterable']
 
 import numpy as np
 import pandas as pd
@@ -13,6 +13,7 @@
 from scipy import optimize
 import functools
 import math
+import collections
 
 # Change matplotlib formatting. TODO incorporate into a style?
 plt.rcParams["patch.force_edgecolor"] = True
@@ -37,6 +38,11 @@ def make_array(*elements):
         # Specifically added for Windows machines where the default 
         # integer is int32 - see GH issue #339.
         return np.array(elements, dtype="int64")
+
+    # Manually cast `elements` as an object due to this: https://github.com/data-8/datascience/issues/458
+    if any(is_non_string_iterable(el) for el in elements):
+        return np.array(elements, dtype=object)
+
     return np.array(elements)
 
 
@@ -241,3 +247,13 @@ def objective(args):
         return result.x.item(0)
     else:
         return result.x
+
+def is_non_string_iterable(value):
+    """Whether a value is iterable."""
+    if isinstance(value, str):
+        return False
+    if hasattr(value, '__iter__'):
+        return True
+    if isinstance(value, collections.abc.Sequence):
+        return True
+    return False