DataFrame.stack() raises TypeError on mixed type level under Python 3 · Issue #18310 · pandas-dev/pandas (original) (raw)

Code Sample, a copy-pastable example if possible

In [2]: df = pd.DataFrame(1, index=range(3), columns=pd.MultiIndex.from_product([['a', 'b'], [1, 2]]))

In [3]: df['c'] = 2

In [4]: df.stack(0)

TypeError Traceback (most recent call last) in () ----> 1 df.stack(0)

/home/nobackup/repo/pandas/pandas/core/frame.py in stack(self, level, dropna) 4500 return stack_multiple(self, level, dropna=dropna) 4501 else: -> 4502 return stack(self, level, dropna=dropna) 4503 4504 def unstack(self, level=-1, fill_value=None):

/home/nobackup/repo/pandas/pandas/core/reshape/reshape.py in stack(frame, level, dropna) 513 514 if isinstance(frame.columns, MultiIndex): --> 515 return _stack_multi_columns(frame, level_num=level_num, dropna=dropna) 516 elif isinstance(frame.index, MultiIndex): 517 new_levels = list(frame.index.levels)

/home/nobackup/repo/pandas/pandas/core/reshape/reshape.py in _stack_multi_columns(frame, level_num, dropna) 619 # level 620 level_to_sort = _convert_level_number(0, this.columns) --> 621 this = this.sort_index(level=level_to_sort, axis=1) 622 623 # tuple list excluding level for grouping columns

/home/nobackup/repo/pandas/pandas/core/frame.py in sort_index(self, axis, level, ascending, inplace, kind, na_position, sort_remaining, by) 3673 # make sure that the axis is lexsorted to start 3674 # if not we need to reconstruct to get the correct indexer -> 3675 labels = labels._sort_levels_monotonic() 3676 indexer = lexsort_indexer(labels._get_labels_for_sorting(), 3677 orders=ascending,

/home/nobackup/repo/pandas/pandas/core/indexes/multi.py in _sort_levels_monotonic(self) 1275 1276 # indexer to reorder the levels -> 1277 indexer = lev.argsort() 1278 lev = lev.take(indexer) 1279

/home/nobackup/repo/pandas/pandas/core/indexes/base.py in argsort(self, *args, **kwargs) 2146 if result is None: 2147 result = np.array(self) -> 2148 return result.argsort(*args, **kwargs) 2149 2150 def add(self, other):

TypeError: unorderable types: str() > int()

Problem description

This is even more confusing because df[['a', 'b']].stack(0) (where no mixed dtype is left) results in the same error.

Expected Output

In [13]: df.stack(0) Out[13]: 1 2
0 a 1.0 1.0 NaN b 1.0 1.0 NaN c NaN NaN 2.0 1 a 1.0 1.0 NaN b 1.0 1.0 NaN c NaN NaN 2.0 2 a 1.0 1.0 NaN b 1.0 1.0 NaN c NaN NaN 2.0

Output of `pd.show_versions()`