pandas ，按计数分组并将计数添加到原始数据框? [英] Pandas, group by count and add count to original dataframe?

问题描述

当尝试对数据框中具有相似种类"的行进行计数时:

When trying to count rows with similar 'kind' in data frame:

import pandas as pd

items = [('aaa','aaa text 1'), ('aaa','aaa text 2'), ('aaa','aaa text 3'),
         ('bb', 'bb text 1'), ('bb', 'bb text 2'), ('bb', 'bb text 3'), 
         ('bb', 'bb text 4'),
         ('cccc','cccc text 1'), ('cccc','cccc text 2'),
         ('dd', 'dd text 1'),
         ('e', 'e text 1'),
         ('fff', 'fff text 1'),
        ]

df = pd.DataFrame(items, columns=['kind', 'msg'])
df

    kind    msg
0   aaa     aaa text 1
1   aaa     aaa text 2
2   aaa     aaa text 3
3   bb      bb text 1
4   bb      bb text 2
5   bb      bb text 3
6   bb      bb text 4
7   cccc    cccc text 1
8   cccc    cccc text 2
9   dd      dd text 1
10  e       e text 1
11  fff     fff text 1

此代码有效:

df = df[['kind']].groupby(['kind'])['kind'] \
                         .count() \
                         .reset_index(name='count') \
                         .sort_values(['count'], ascending=False) \
                         .head(5)

df

结果:

    kind      count
    0   aaa   1
    1   bb    1
    2   cccc  1
    3   dd    1
    4   e     1

但是，如何才能像原始一加'count'列那样获得一个包含所有列的数据框?因此，结果应按此顺序具有种类"，味精"，计数"列?

Yet, how can one get a data frame with all columns as in original one plus 'count' column? So the result should have columns 'kind', 'msg', 'count' in this order?

另外，如何按计数的降序对生成的数据帧进行排序?

Also, how to sort this resulting data frame in descending order of count?

推荐答案

IIUC

In [247]: df['count'] = df.groupby('kind').transform('count')

In [248]: df
Out[248]:
    kind          msg  count
0    aaa   aaa text 1      3
1    aaa   aaa text 2      3
2    aaa   aaa text 3      3
3     bb    bb text 1      4
4     bb    bb text 2      4
5     bb    bb text 3      4
6     bb    bb text 4      4
7   cccc  cccc text 1      2
8   cccc  cccc text 2      2
9     dd    dd text 1      1
10     e     e text 1      1
11   fff   fff text 1      1

排序:

In [249]: df.sort_values('count', ascending=False)
Out[249]:
    kind          msg  count
3     bb    bb text 1      4
4     bb    bb text 2      4
5     bb    bb text 3      4
6     bb    bb text 4      4
0    aaa   aaa text 1      3
1    aaa   aaa text 2      3
2    aaa   aaa text 3      3
7   cccc  cccc text 1      2
8   cccc  cccc text 2      2
9     dd    dd text 1      1
10     e     e text 1      1
11   fff   fff text 1      1

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