如何在pandas中将groupby().transform()转换为value_counts()？

Question

我正在处理一个df1带有物品价格的熊猫数据框.

  Item    Price  Minimum Most_Common_Price
0 Coffee  1      1       2
1 Coffee  2      1       2
2 Coffee  2      1       2
3 Tea     3      3       4
4 Tea     4      3       4
5 Tea     4      3       4

我创建Minimum使用:

df1["Minimum"] = df1.groupby(["Item"])['Price'].transform(min)

我该如何创作Most_Common_Price？

df1["Minimum"] = df1.groupby(["Item"])['Price'].transform(value_counts()) # Doesn't work

目前,我采用了多步骤方法:

for item in df1.Item.unique().tolist(): # Pseudocode
 df1 = df1[df1.Price == Item]           # Pseudocode
 df1.Price.value_counts().max()         # Pseudocode

这太过分了.必须有一种更简单的方法,理想情况是一行

如何在pandas中将groupby().transform()转换为value_counts()？

Answer 1

一个不错的方法是使用pd.Series.mode, 如果您想要最常见的元素（即模式）。

In [32]: df
Out[32]:
     Item  Price  Minimum
0  Coffee      1        1
1  Coffee      2        1
2  Coffee      2        1
3     Tea      3        3
4     Tea      4        3
5     Tea      4        3

In [33]: df['Most_Common_Price'] = df.groupby(["Item"])['Price'].transform(pd.Series.mode)

In [34]: df
Out[34]:
     Item  Price  Minimum  Most_Common_Price
0  Coffee      1        1                  2
1  Coffee      2        1                  2
2  Coffee      2        1                  2
3     Tea      3        3                  4
4     Tea      4        3                  4
5     Tea      4        3                  4

正如@Wen 所指出的，pd.Series.mode可以返回一个pd.Series值，所以只需获取第一个：

Out[67]:
     Item  Price  Minimum
0  Coffee      1        1
1  Coffee      2        1
2  Coffee      2        1
3     Tea      3        3
4     Tea      4        3
5     Tea      4        3
6     Tea      3        3

In [68]: df[df.Item =='Tea'].Price.mode()
Out[68]:
0    3
1    4
dtype: int64

In [69]: df['Most_Common_Price'] = df.groupby(["Item"])['Price'].transform(lambda S: S.mode()[0])

In [70]: df
Out[70]:
     Item  Price  Minimum  Most_Common_Price
0  Coffee      1        1                  2
1  Coffee      2        1                  2
2  Coffee      2        1                  2
3     Tea      3        3                  3
4     Tea      4        3                  3
5     Tea      4        3                  3
6     Tea      3        3                  3

Answer 2

你可以使用groupby+ transform+ value_counts+ idxmax-

df['Most_Common_Price'] = (
    df.groupby('Item')['Price'].transform(lambda x: x.value_counts().idxmax()))

df

     Item  Price  Minimum  Most_Common_Price
0  Coffee      1        1                  2
1  Coffee      2        1                  2
2  Coffee      2        1                  2
3     Tea      3        3                  4
4     Tea      4        3                  4
5     Tea      4        3                  4

改进(谢谢,Vaishali!)涉及使用pd.Series.map-

# Thanks, Vaishali!
df['Item'] = (df['Item'].map(df.groupby('Item')['Price']
                        .agg(lambda x: x.value_counts().idxmax()))
df

     Item  Price  Minimum  Most_Common_Price
0  Coffee      1        1                  2
1  Coffee      2        1                  2
2  Coffee      2        1                  2
3     Tea      3        3                  4
4     Tea      4        3                  4
5     Tea      4        3                  4