用groupby pandas 计算行数 [英] count number rows with groupby pandas

问题描述

我在熊猫0.17中具有以下功能:

I had the following function in pandas 0.17:

df['numberrows'] = df.groupby(['column1','column2','column3'], as_index=False)[['column1']].transform('count').astype('int')

但是我今天升级了大熊猫，现在我得到了错误:

But I upgraded pandas today and now I get the error:

  File "/usr/local/lib/python3.4/dist-packages/pandas/core/internals.py",

第3810行，插入 引发ValueError('无法插入{}，已经存在'.format(item))

line 3810, in insert raise ValueError('cannot insert {}, already exists'.format(item))

ValueError:无法插入column1，已经存在

ValueError: cannot insert column1, already exists

更新中有哪些更改导致该功能不再起作用?

What has changed in the update which causes this function to not work anymore?

我想对列进行分组，并添加具有分组依据的数量或行的列.

I want to groupby the columns and add a column which has the amount or rows of the groupby.

如果我以前做的不是一个好的函数，那么也欢迎使用另一种分组方式，同时获取已分组的行数.

If what I did before was not a good function, another way of grouping while getting the amount of rows that were grouped is also welcome.

小型数据集:

    column1  column2    column3   
 0  test     car1       1           
 1  test2    car5       2         
 2  test     car1       1         
 3  test4    car2       1      
 4  test2    car1       1         

结果将是:

    column1  column2    column3   numberrows
 0  test     car1       1           2
 1  test2    car5       2           1     
 3  test4    car2       1           1
 4  test2    car1       1           1

推荐答案

请考虑以下方法:

In [18]: df['new'] = df.groupby(['column1','column2','column3'])['column1'] \
                       .transform('count')

In [19]: df
Out[19]:
  column1 column2  column3  new
0    test    car1        1    2
1   test2    car5        2    1
2    test    car1        1    2
3   test4    car2        1    1
4   test2    car1        1    1

更新:

In [26]: df.groupby(['column1','column2','column3'])['column1'] \
           .count().reset_index(name='numberrows')
Out[26]:
  column1 column2  column3  numberrows
0    test    car1        1           2
1   test2    car1        1           1
2   test2    car5        2           1
3   test4    car2        1           1

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