[英]pandas groupby two columns and summarize by mean
I have a data frame like this:我有一个这样的数据框:
df = pd.DataFrame()
df['id'] = [1,1,1,2,2,3,3,3,3,4,4,5]
df['view'] = ['A', 'B', 'A', 'A','B', 'A', 'B', 'A', 'A','B', 'A', 'B']
df['value'] = np.random.random(12)
id view value
0 1 A 0.625781
1 1 B 0.330084
2 1 A 0.024532
3 2 A 0.154651
4 2 B 0.196960
5 3 A 0.393941
6 3 B 0.607217
7 3 A 0.422823
8 3 A 0.994323
9 4 B 0.366650
10 4 A 0.649585
11 5 B 0.513923
I now want to summarize for each id
each view
by mean of 'value'.我现在想通过“值”来总结每个
view
的每个id
。 Think of this as some ids have repeated observations for view, and I want to summarize them.把这想成是一些id重复观察以供查看,我想总结一下。 For example, id 1 has two observations for A.
例如,id 1 对 A 有两个观察值。
I tried我试过
res = df.groupby(['id', 'view'])['value'].mean()
This actually almost what I want, but pandas combines the id
and view
column into one, which I do not want.这实际上几乎是我想要的,但是 pandas 将
id
和view
列合二为一,这是我不想要的。
id view
1 A 0.325157
B 0.330084
2 A 0.154651
B 0.196960
3 A 0.603696
B 0.607217
4 A 0.649585
B 0.366650
5 B 0.513923
also res.shape is of dimension (9,) res.shape 也是维度 (9,)
my desired output would be this:我想要的输出是这样的:
id view value
1 A 0.325157
1 B 0.330084
2 A 0.154651
2 B 0.196960
3 A 0.603696
3 B 0.607217
4 A 0.649585
4 B 0.366650
5 B 0.513923
where the column names and dimensions are kept and where the id is repeated.保留列名和维度的位置以及重复 id 的位置。 Each id should have only 1 row for A and B.
每个 id 应该只有 1 行用于 A 和 B。
How can I achieve this?我怎样才能做到这一点?
You need reset_index
or parameter as_index=False
in groupby
, because you get MuliIndex
and by default the higher levels of the indexes are sparsified to make the console output a bit easier on the eyes:您需要在
groupby
reset_index
或参数as_index=False
,因为您获得了MuliIndex
并且默认情况下,索引的较高级别会被稀疏化,以使控制台输出在眼睛上更容易一些:
np.random.seed(100)
df = pd.DataFrame()
df['id'] = [1,1,1,2,2,3,3,3,3,4,4,5]
df['view'] = ['A', 'B', 'A', 'A','B', 'A', 'B', 'A', 'A','B', 'A', 'B']
df['value'] = np.random.random(12)
print (df)
id view value
0 1 A 0.543405
1 1 B 0.278369
2 1 A 0.424518
3 2 A 0.844776
4 2 B 0.004719
5 3 A 0.121569
6 3 B 0.670749
7 3 A 0.825853
8 3 A 0.136707
9 4 B 0.575093
10 4 A 0.891322
11 5 B 0.209202
res = df.groupby(['id', 'view'])['value'].mean().reset_index()
print (res)
id view value
0 1 A 0.483961
1 1 B 0.278369
2 2 A 0.844776
3 2 B 0.004719
4 3 A 0.361376
5 3 B 0.670749
6 4 A 0.891322
7 4 B 0.575093
8 5 B 0.209202
res = df.groupby(['id', 'view'], as_index=False)['value'].mean()
print (res)
id view value
0 1 A 0.483961
1 1 B 0.278369
2 2 A 0.844776
3 2 B 0.004719
4 3 A 0.361376
5 3 B 0.670749
6 4 A 0.891322
7 4 B 0.575093
8 5 B 0.209202
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.