我有以下DataFrame:
item response
1 A
1 A
1 B
2 A
2 A
我想添加一个列,该列具有最给定的项目响应。这应导致:
item response mostGivenResponse
1 A A
1 A A
1 B A
2 C C
2 C C
我尝试过这样的事情:
df["responseCount"] = df.groupby(["ItemCode", "Response"])["Response"].transform("count")
df["mostGivenResponse"] = df.groupby(['ItemCode'])['responseCount'].transform(max)
但是,大多数GivenResponse现在是响应的计数,而不是响应本身。
使用value_counts
并返回第一个索引值:
df["responseCount"] = (df.groupby("item")["response"]
.transform(lambda x: x.value_counts().index[0]))
print (df)
item response responseCount
0 1 A A
1 1 A A
2 1 B A
3 2 C C
4 2 C C
或collections.Counter.most_common
:
from collections import Counter
df["responseCount"] = (df.groupby("item")["response"]
.transform(lambda x: Counter(x).most_common(1)[0][0]))
print (df)
item response responseCount
0 1 A A
1 1 A A
2 1 B A
3 2 C C
4 2 C C
编辑:
问题是只有一个或多个NaN
s组,解决方案是使用过滤if-else
:
print (df)
item response
0 1 A
1 1 A
2 2 NaN
3 2 NaN
4 3 NaN
def f(x):
s = x.value_counts()
print (s)
A 2
Name: 1, dtype: int64
Series([], Name: 2, dtype: int64)
Series([], Name: 3, dtype: int64)
#return np.nan if s.empty else s.index[0]
return np.nan if len(s) == 0 else s.index[0]
df["responseCount"] = df.groupby("item")["response"].transform(f)
print (df)
item response responseCount
0 1 A A
1 1 A A
2 2 NaN NaN
3 2 NaN NaN
4 3 NaN NaN
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