changes

Author: topper-123

pandas/core/internals/construction.py

@@ -195,14 +195,13 @@ def init_dict(data, index, columns, dtype=None):
             arrays.loc[missing] = [val] * missing.sum()
 
     else:
-        data = OrderedDict((col_name, com.maybe_itarable_to_list(col))
-                           for col_name, col in data.items())
         keys = com.dict_keys_to_ordered_list(data)
         columns = data_names = Index(keys)
+        arrays = (com.maybe_itarable_to_list(data[k]) for k in keys)
         # GH#24096 need copy to be deep for datetime64tz case
         # TODO: See if we can avoid these copies
-        arrays = [data[k] if not is_datetime64tz_dtype(data[k]) else
-                  data[k].copy(deep=True) for k in keys]
+        arrays = [arr if not is_datetime64tz_dtype(arr) else
+                  arr.copy(deep=True) for arr in arrays]
     return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
 
 

pandas/core/series.py

@@ -1,7 +1,7 @@
 """
 Data structure for 1-dimensional cross-sectional and time series data
 """
-from collections import OrderedDict, abc
+from collections import OrderedDict
 from io import StringIO
 from shutil import get_terminal_size
 from textwrap import dedent
@@ -219,14 +219,11 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
             elif isinstance(data, (set, frozenset)):
                 raise TypeError("{0!r} type is unordered"
                                 "".format(data.__class__.__name__))
-            elif (isinstance(data, abc.Iterable) and
-                  not isinstance(data, abc.Sized)):
-                data = com.maybe_itarable_to_list(data)
             elif isinstance(data, ABCSparseArray):
                 # handle sparse passed here (and force conversion)
                 data = data.to_dense()
             else:
-                pass
+                data = com.maybe_itarable_to_list(data)
 
             if index is None:
                 if not is_list_like(data):