在读完所有的答案后，我想到了这个:

def __my_flatten_cols(self, how="_".join, reset_index=True):
    how = (lambda iter: list(iter)[-1]) if how == "last" else how
    self.columns = [how(filter(None, map(str, levels))) for levels in self.columns.values] \
                    if isinstance(self.columns, pd.MultiIndex) else self.columns
    return self.reset_index() if reset_index else self
pd.DataFrame.my_flatten_cols = __my_flatten_cols

用法:

给定一个数据帧:

df = pd.DataFrame({"grouper": ["x","x","y","y"], "val1": [0,2,4,6], 2: [1,3,5,7]}, columns=["grouper", "val1", 2])

  grouper  val1  2
0       x     0  1
1       x     2  3
2       y     4  5
3       y     6  7

Single aggregation method: resulting variables named the same as source: df.groupby(by="grouper").agg("min").my_flatten_cols() Same as df.groupby(by="grouper", as_index=False) or .agg(...).reset_index() ----- before ----- val1 2 grouper ------ after ----- grouper val1 2 0 x 0 1 1 y 4 5 Single source variable, multiple aggregations: resulting variables named after statistics: df.groupby(by="grouper").agg({"val1": [min,max]}).my_flatten_cols("last") Same as a = df.groupby(..).agg(..); a.columns = a.columns.droplevel(0); a.reset_index(). ----- before ----- val1 min max grouper ------ after ----- grouper min max 0 x 0 2 1 y 4 6 Multiple variables, multiple aggregations: resulting variables named (varname)_(statname): df.groupby(by="grouper").agg({"val1": min, 2:[sum, "size"]}).my_flatten_cols() # you can combine the names in other ways too, e.g. use a different delimiter: #df.groupby(by="grouper").agg({"val1": min, 2:[sum, "size"]}).my_flatten_cols(" ".join) Runs a.columns = ["_".join(filter(None, map(str, levels))) for levels in a.columns.values] under the hood (since this form of agg() results in MultiIndex on columns). If you don't have the my_flatten_cols helper, it might be easier to type in the solution suggested by @Seigi: a.columns = ["_".join(t).rstrip("_") for t in a.columns.values], which works similarly in this case (but fails if you have numeric labels on columns) To handle the numeric labels on columns, you could use the solution suggested by @jxstanford and @Nolan Conaway (a.columns = ["_".join(tuple(map(str, t))).rstrip("_") for t in a.columns.values]), but I don't understand why the tuple() call is needed, and I believe rstrip() is only required if some columns have a descriptor like ("colname", "") (which can happen if you reset_index() before trying to fix up .columns) ----- before ----- val1 2 min sum size grouper ------ after ----- grouper val1_min 2_sum 2_size 0 x 0 4 2 1 y 4 12 2 You want to name the resulting variables manually: (this is deprecated since pandas 0.20.0 with no adequate alternative as of 0.23) df.groupby(by="grouper").agg({"val1": {"sum_of_val1": "sum", "count_of_val1": "count"}, 2: {"sum_of_2": "sum", "count_of_2": "count"}}).my_flatten_cols("last") Other suggestions include: setting the columns manually: res.columns = ['A_sum', 'B_sum', 'count'] or .join()ing multiple groupby statements. ----- before ----- val1 2 count_of_val1 sum_of_val1 count_of_2 sum_of_2 grouper ------ after ----- grouper count_of_val1 sum_of_val1 count_of_2 sum_of_2 0 x 2 2 2 4 1 y 2 10 2 12

由helper函数处理的情况

level names can be non-string, e.g. Index pandas DataFrame by column numbers, when column names are integers, so we have to convert with map(str, ..) they can also be empty, so we have to filter(None, ..) for single-level columns (i.e. anything except MultiIndex), columns.values returns the names (str, not tuples) depending on how you used .agg() you may need to keep the bottom-most label for a column or concatenate multiple labels (since I'm new to pandas?) more often than not, I want reset_index() to be able to work with the group-by columns in the regular way, so it does that by default

还有一个简短的，只使用熊猫的方法:

df.columns = df.columns.to_flat_index().str.join('_')

产量为输出:

    USAF_  WBAN_  day_  month_  ...  s_PC_sum  tempf_amax  tempf_amin  year_
0  702730  26451     1       1  ...       1.0       30.92       24.98   1993
1  702730  26451     2       1  ...       0.0       32.00       24.98   1993
2  702730  26451     3       1  ...       1.0       23.00        6.98   1993
3  702730  26451     4       1  ...       1.0       10.04        3.92   1993
4  702730  26451     5       1  ...       3.0       19.94       10.94   1993

您会注意到，对于不属于MultiIndex的列，后面有下划线。你提到过你不关心名字，所以这可能对你有用。在我自己类似的用例中，所有列都有两层，因此这个简单的命令创建了漂亮的名称。

在读完所有的答案后，我想到了这个:

def __my_flatten_cols(self, how="_".join, reset_index=True):
    how = (lambda iter: list(iter)[-1]) if how == "last" else how
    self.columns = [how(filter(None, map(str, levels))) for levels in self.columns.values] \
                    if isinstance(self.columns, pd.MultiIndex) else self.columns
    return self.reset_index() if reset_index else self
pd.DataFrame.my_flatten_cols = __my_flatten_cols

用法:

给定一个数据帧:

df = pd.DataFrame({"grouper": ["x","x","y","y"], "val1": [0,2,4,6], 2: [1,3,5,7]}, columns=["grouper", "val1", 2])

  grouper  val1  2
0       x     0  1
1       x     2  3
2       y     4  5
3       y     6  7

Single aggregation method: resulting variables named the same as source: df.groupby(by="grouper").agg("min").my_flatten_cols() Same as df.groupby(by="grouper", as_index=False) or .agg(...).reset_index() ----- before ----- val1 2 grouper ------ after ----- grouper val1 2 0 x 0 1 1 y 4 5 Single source variable, multiple aggregations: resulting variables named after statistics: df.groupby(by="grouper").agg({"val1": [min,max]}).my_flatten_cols("last") Same as a = df.groupby(..).agg(..); a.columns = a.columns.droplevel(0); a.reset_index(). ----- before ----- val1 min max grouper ------ after ----- grouper min max 0 x 0 2 1 y 4 6 Multiple variables, multiple aggregations: resulting variables named (varname)_(statname): df.groupby(by="grouper").agg({"val1": min, 2:[sum, "size"]}).my_flatten_cols() # you can combine the names in other ways too, e.g. use a different delimiter: #df.groupby(by="grouper").agg({"val1": min, 2:[sum, "size"]}).my_flatten_cols(" ".join) Runs a.columns = ["_".join(filter(None, map(str, levels))) for levels in a.columns.values] under the hood (since this form of agg() results in MultiIndex on columns). If you don't have the my_flatten_cols helper, it might be easier to type in the solution suggested by @Seigi: a.columns = ["_".join(t).rstrip("_") for t in a.columns.values], which works similarly in this case (but fails if you have numeric labels on columns) To handle the numeric labels on columns, you could use the solution suggested by @jxstanford and @Nolan Conaway (a.columns = ["_".join(tuple(map(str, t))).rstrip("_") for t in a.columns.values]), but I don't understand why the tuple() call is needed, and I believe rstrip() is only required if some columns have a descriptor like ("colname", "") (which can happen if you reset_index() before trying to fix up .columns) ----- before ----- val1 2 min sum size grouper ------ after ----- grouper val1_min 2_sum 2_size 0 x 0 4 2 1 y 4 12 2 You want to name the resulting variables manually: (this is deprecated since pandas 0.20.0 with no adequate alternative as of 0.23) df.groupby(by="grouper").agg({"val1": {"sum_of_val1": "sum", "count_of_val1": "count"}, 2: {"sum_of_2": "sum", "count_of_2": "count"}}).my_flatten_cols("last") Other suggestions include: setting the columns manually: res.columns = ['A_sum', 'B_sum', 'count'] or .join()ing multiple groupby statements. ----- before ----- val1 2 count_of_val1 sum_of_val1 count_of_2 sum_of_2 grouper ------ after ----- grouper count_of_val1 sum_of_val1 count_of_2 sum_of_2 0 x 2 2 2 4 1 y 2 10 2 12

由helper函数处理的情况

level names can be non-string, e.g. Index pandas DataFrame by column numbers, when column names are integers, so we have to convert with map(str, ..) they can also be empty, so we have to filter(None, ..) for single-level columns (i.e. anything except MultiIndex), columns.values returns the names (str, not tuples) depending on how you used .agg() you may need to keep the bottom-most label for a column or concatenate multiple labels (since I'm new to pandas?) more often than not, I want reset_index() to be able to work with the group-by columns in the regular way, so it does that by default

为了在其他DataFrame方法链中平展MultiIndex，定义一个这样的函数:

def flatten_index(df):
  df_copy = df.copy()
  df_copy.columns = ['_'.join(col).rstrip('_') for col in df_copy.columns.values]
  return df_copy.reset_index()

然后使用管道方法在DataFrame方法链中应用这个函数，在groupby和agg之后，但在链中任何其他方法之前:

my_df \
  .groupby('group') \
  .agg({'value': ['count']}) \
  .pipe(flatten_index) \
  .sort_values('value_count')

我认为最简单的方法是将列设置为顶层:

df.columns = df.columns.get_level_values(0)

注意:如果to级别有名称，您也可以通过this访问它，而不是0。

.

如果你想合并/加入你的MultiIndex到一个索引(假设你的列中只有字符串条目)，你可以:

df.columns = [' '.join(col).strip() for col in df.columns.values]

注意:当没有第二个索引时，我们必须去掉空白。

In [11]: [' '.join(col).strip() for col in df.columns.values]
Out[11]: 
['USAF',
 'WBAN',
 'day',
 'month',
 's_CD sum',
 's_CL sum',
 's_CNT sum',
 's_PC sum',
 'tempf amax',
 'tempf amin',
 'year']

pd.DataFrame(df.to_records()) # multiindex become columns and new index is integers only