Python groupby.value_counts() 之后的熊猫 reset_index
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pandas reset_index after groupby.value_counts()
提问by muon
I am trying to groupby a column and compute value counts on another column.
我正在尝试对一列进行分组并计算另一列上的值计数。
import pandas as pd
dftest = pd.DataFrame({'A':[1,1,1,1,1,1,1,1,1,2,2,2,2,2],
'Amt':[20,20,20,30,30,30,30,40, 40,10, 10, 40,40,40]})
print(dftest)
dftest looks like
dftest 看起来像
A Amt
0 1 20
1 1 20
2 1 20
3 1 30
4 1 30
5 1 30
6 1 30
7 1 40
8 1 40
9 2 10
10 2 10
11 2 40
12 2 40
13 2 40
perform grouping
进行分组
grouper = dftest.groupby('A')
df_grouped = grouper['Amt'].value_counts()
which gives
这使
A Amt
1 30 4
20 3
40 2
2 40 3
10 2
Name: Amt, dtype: int64
what I want is to keep top two rows of each group
我想要的是保留每组的前两行
Also, I was perplexed by an error when I tried to reset_index
此外，当我尝试执行时，我对错误感到困惑 reset_index
df_grouped.reset_index()
which gives following error
这给出了以下错误
df_grouped.reset_index() ValueError: cannot insert Amt, already exists
df_grouped.reset_index() ValueError: 无法插入 Amt，已经存在
回答by jezrael
You need parameter namein reset_index, because Seriesname is same as name of one of levels of MultiIndex:
您需要参数namein reset_index，因为Series名称与以下级别之一的名称相同MultiIndex：
df_grouped.reset_index(name='count')
Another solution is renameSeriesname:
另一个解决方案是名称：renameSeries
print (df_grouped.rename('count').reset_index())
A Amt count
0 1 30 4
1 1 20 3
2 1 40 2
3 2 40 3
4 2 10 2
More common solution instead value_countsis aggregate size:
更常见的解决方案value_counts是聚合size：
df_grouped1 = dftest.groupby(['A','Amt']).size().reset_index(name='count')
print (df_grouped1)
A Amt count
0 1 20 3
1 1 30 4
2 1 40 2
3 2 10 2
4 2 40 3
