如何在 pandas 中将byby().transform()转换为value_counts()? [英] How to groupby().transform() to value_counts() in pandas?

问题描述

我正在处理带有商品价格的熊猫数据框 df1 .

I am processing a pandas dataframe df1 with prices of items.

  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

我使用以下方法创建 Minimum :

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

如何创建最低共同价?

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

此刻，我使用多步骤方法:

In the moment, I use a multi-step approach:

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，理想情况下是一行

which is overkill. There must be a more simple way, ideally in one line

如何在熊猫中将byby().transform()转换为value_counts()?

推荐答案

您可以对 value_counts 和使用 groupby + transform idxmax .

You could use groupby + transform with value_counts and 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

---

一项改进涉及使用 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

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