最python化的方法是使用map函数。

df.columns = df.columns.map(' '.join).str.strip()

输出打印(df.columns):

Index(['USAF', 'WBAN', 'day', 'month', 's_CD sum', 's_CL sum', 's_CNT sum',
       's_PC sum', 'tempf amax', 'tempf amin', 'year'],
      dtype='object')

使用Python 3.6+和f string更新:

df.columns = [f'{f} {s}' if s != '' else f'{f}' 
              for f, s in df.columns]

print(df.columns)

输出:

Index(['USAF', 'WBAN', 'day', 'month', 's_CD sum', 's_CL sum', 's_CNT sum',
       's_PC sum', 'tempf amax', 'tempf amin', 'year'],
      dtype='object')

我将分享一个对我有效的简单方法。

[" ".join([str(elem) for elem in tup]) for tup in df.columns.tolist()]
#df = df.reset_index() if needed

处理多个级别和混合类型的通用解决方案:

df.columns = ['_'.join(tuple(map(str, t))) for t in df.columns.values]

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。

我发现自己遇到了同样的问题，即如何在agg之后平展列名，但我还希望将聚合类型保留为行名。

为此，可以使用stack()。结果是列名是扁平的，但也保留了聚合类型。例如，现在您可以安全地导出到csv。