[英]Pandas groupby values in a list
I am trying to return a groupby
from a pandas
df
. 我试图从
pandas
df
返回一个groupby
。 I want the output values to be summed not merged
. 我希望将输出值相加而不
merged
。 But the following merges
the appropriate lists
. 但以下内容
merges
了相应的lists
。
import pandas as pd
d = ({
'Id' : [1,2,2,1],
'Val' : ['A','B','B','A'],
'Output' : [[1,2,3,4,5],[5,3,3,2,1],[6,7,8,9,1],[6,7,8,9,1]],
})
df = pd.DataFrame(data = d)
df = df.groupby(['Id','Val']).agg({'Output':'sum'}, axis = 1)
Out: 日期:
Output
Id Val
1 A [1, 2, 3, 4, 5, 6, 7, 8, 9, 1]
2 B [5, 3, 3, 2, 1, 6, 7, 8, 9, 1]
Intended Output: 预期产出:
Output
Id Val
1 A [7,9,11,13,6]
2 B [11,10,11,11,2]
Or use a one-liner which converts to np.array
: 或者使用转换为
np.array
:
df = df.groupby(['Id','Val']).apply(lambda x: x.Output.apply(np.array).sum())
print(df)
Output: 输出:
Id Val
1 A [7, 9, 11, 13, 6]
2 B [11, 10, 11, 11, 2]
dtype: object
You can change the list
to numpy
array
then 您可以将
list
更改为numpy
array
df.Output=df.Output.apply(np.array)
df.groupby(['Id','Val']).Output.apply(lambda x : np.sum(x))
Out[389]:
Id Val
1 A [7, 9, 11, 13, 6]
2 B [11, 10, 11, 11, 2]
Name: Output, dtype: object
Another solution using using zip rather than using apply twice, 使用zip而不是使用apply两次的另一个解决方案,
df.groupby(['Id','Val']).Output.apply(lambda x: [sum(i) for i in list(zip(*x))])
Id Val
1 A [7, 9, 11, 13, 6]
2 B [11, 10, 11, 11, 2]
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