def _convert_level_number(level_num, columns): """ Logic for converting the level number to something we can safely pass to swaplevel. If `level_num` matches a column name return the name from position `level_num`, otherwise return `level_num`. """ if level_num in columns.names: return columns.names[level_num] return level_num this = frame.copy() # this makes life much simpler if level_num != frame.columns.nlevels - 1: # roll levels to put selected level at end roll_columns = this.columns for i in range(level_num, frame.columns.nlevels - 1): # Need to check if the ints conflict with level names lev1 = _convert_level_number(i, roll_columns) lev2 = _convert_level_number(i + 1, roll_columns) roll_columns = roll_columns.swaplevel(lev1, lev2) this.columns = roll_columns if not this.columns._is_lexsorted(): # Workaround the edge case where 0 is one of the column names, # which interferes with trying to sort based on the first # level level_to_sort = _convert_level_number(0, this.columns) this = this.sort_index(level=level_to_sort, axis=1) new_columns = _stack_multi_column_index(this.columns) # time to ravel the values new_data = {} level_vals = this.columns.levels[-1] level_codes = sorted(set(this.columns.codes[-1])) level_vals_nan = level_vals.insert(len(level_vals), None) level_vals_used = np.take(level_vals_nan, level_codes) levsize = len(level_codes) drop_cols = [] for key in new_columns: try: loc = this.columns.get_loc(key) except KeyError: drop_cols.append(key) continue # can make more efficient? # we almost always return a slice # but if unsorted can get a boolean # indexer if not isinstance(loc, slice): slice_len = len(loc) else: slice_len = loc.stop - loc.start if slice_len != levsize: chunk = this.loc[:, this.columns[loc]] chunk.columns = level_vals_nan.take(chunk.columns.codes[-1]) value_slice = chunk.reindex(columns=level_vals_used).values else: if frame._is_homogeneous_type and is_extension_array_dtype( frame.dtypes.iloc[0] ): dtype = this[this.columns[loc]].dtypes.iloc[0] subset = this[this.columns[loc]] value_slice = dtype.construct_array_type()._concat_same_type( [x._values for _, x in subset.items()] ) N, K = this.shape #----------------> !!!! look at here, is it a bug? #N, K = subset.shape # idx = np.arange(N * K).reshape(K, N).T.ravel() value_slice = value_slice.take(idx) elif frame._is_mixed_type: value_slice = this[this.columns[loc]].values else: value_slice = this.values[:, loc] if value_slice.ndim > 1: # i.e. not extension value_slice = value_slice.ravel() new_data[key] = value_slice if len(drop_cols) > 0: new_columns = new_columns.difference(drop_cols) N = len(this) if isinstance(this.index, MultiIndex): new_levels = list(this.index.levels) new_names = list(this.index.names) new_codes = [lab.repeat(levsize) for lab in this.index.codes] else: old_codes, old_levels = factorize_from_iterable(this.index) new_levels = [old_levels] new_codes = [old_codes.repeat(levsize)] new_names = [this.index.name] # something better? new_levels.append(level_vals) new_codes.append(np.tile(level_codes, N)) new_names.append(frame.columns.names[level_num]) new_index = MultiIndex( levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False ) result = frame._constructor(new_data, index=new_index, columns=new_columns) # more efficient way to go about this? can do the whole masking biz but # will only save a small amount of time... if dropna: result = result.dropna(axis=0, how="all") return result