如何循环分组的 Pandas 数据框?

问题描述

数据帧:

  c_os_family_ss c_os_major_is l_customer_id_i
0      Windows 7                         90418
1      Windows 7                         90418
2      Windows 7                         90418

代码:

print df
for name, group in df.groupby('l_customer_id_i').agg(lambda x: ','.join(x)):
    print name
    print group

我正在尝试遍历聚合数据，但出现错误:

I'm trying to just loop over the aggregated data, but I get the error:

ValueError:解包的值太多

ValueError: too many values to unpack

@EdChum，这是预期的输出:

@EdChum, here's the expected output:

                                                    c_os_family_ss  
l_customer_id_i
131572           Windows 7,Windows 7,Windows 7,Windows 7,Window...
135467           Windows 7,Windows 7,Windows 7,Windows 7,Window...

                                                     c_os_major_is
l_customer_id_i
131572           ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...
135467           ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...

输出不是问题，我希望循环遍历每个组.

The output is not the problem, I wish to loop over every group.

推荐答案

df.groupby('l_customer_id_i').agg(lambda x: ','.join(x)) 确实已经返回一个数据框，所以你不能再循环组了.

df.groupby('l_customer_id_i').agg(lambda x: ','.join(x)) does already return a dataframe, so you cannot loop over the groups anymore.

一般:

• df.groupby(...) 返回一个 GroupBy 对象(DataFrameGroupBy 或 SeriesGroupBy)，通过这个，您可以遍历组(如文档此处中所述).您可以执行以下操作:

• df.groupby(...) returns a GroupBy object (a DataFrameGroupBy or SeriesGroupBy), and with this, you can iterate through the groups (as explained in the docs here). You can do something like:

grouped = df.groupby('A')

for name, group in grouped:
    ...