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

@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:
    ...

当您在 groupby 上应用函数时，在您的示例中 df.groupby(...).agg(...) (但这也可以是 transform, apply, mean, ...)，你组合应用函数的结果将不同的组放在一个数据框中(groupby 的split-apply-combine"范式的应用和组合步骤).因此，其结果将始终是 DataFrame(或 Series，具体取决于应用的功能).

When you apply a function on the groupby, in your example df.groupby(...).agg(...) (but this can also be transform, apply, mean, ...), you combine the result of applying the function to the different groups together in one dataframe (the apply and combine step of the 'split-apply-combine' paradigm of groupby). So the result of this will always be again a DataFrame (or a Series depending on the applied function).

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