你可以只stack
列(将其移动到指数),并调用reset_index
与降=真,或者你可以使用一个reset_index()
作为出发点(见frame.py#L2940)写一个reset_columns()
方法
df.query('some query')
.apply(cool_func)
.stack(level='unwanted_col_level_name')
.reset_index('unwanted_col_level_name',drop=True)
.apply(another_cool_func)
替代:猴补丁溶液
def drop_column_levels(self, level=None, inplace=False):
"""
For DataFrame with multi-level columns, drops one or more levels.
For a standard index, or if dropping all levels of the MultiIndex, will revert
back to using a classic RangeIndexer for column names.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
default
inplace : boolean, default False
Modify the DataFrame in place (do not create a new object)
Returns
-------
resetted : DataFrame
"""
if inplace:
new_obj = self
else:
new_obj = self.copy()
new_columns = pd.core.common._default_index(len(new_obj.columns))
if isinstance(self.index, pd.MultiIndex):
if level is not None:
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for lev in level]
if len(level) < len(self.columns.levels):
new_columns = self.columns.droplevel(level)
new_obj.columns = new_columns
if not inplace:
return new_obj
# Monkey patch the DataFrame class
pd.DataFrame.drop_column_levels = drop_column_levels
您是否在寻找'.drop'来放置一列? – James
嗨 - 没有我想要在'DataFrame.columns'轴'MultiIndex'中删除一个级别。 – dmeu
如何删除列索引级别时如何处理列名的重复? – James