Question

我有以下DataFrame：

item    response
1       A       
1       A       
1       B       
2       A       
2       A

我想添加一列，该列的项目响应最多。这应该导致：

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。

Answer 1

有pd.Series.mode：

df.groupby('item').response.transform(pd.Series.mode)
Out[28]: 
0    A
1    A
2    A
3    C
4    C
Name: response, dtype: object

Answer 2

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：

问题仅包含一个或多个NaN组，解决方案是使用if-else进行过滤：

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

Answer 3

您可以使用标准库中的statistics.mode：

from statistics import mode

df['mode'] = df.groupby('item')['response'].transform(mode)

print(df)

   item response mode
0     1        A    A
1     1        A    A
2     1        B    A
3     2        C    C
4     2        C    C

大熊猫：获取组中出现次数最多的字符串值

3 个答案: