添加基于其他列和行的新列

Question

我有一个大数据框。让我写一个示例数据框让你理解我的问题。

A      B      C     
car    red    15
car    blue   20
car    grey   14
bike   red    6
bike   blue   8
phone  red    9
phone  blue   11
phone  grey   10

假设 C 列显示价格。我想添加一个名为“D”的列。此列将回答“读取的汽车是否比所有汽车的平均价格贵？”。以及其他 A 值的相同问题。我的问题基本上就是这样。我想看这个：

A      B      C    D    
car    red    15   cheap
car    blue   20   expensive
car    grey   14   cheap
bike   red    6    cheap
bike   blue   8    expensive
phone  red    9    cheap
phone  blue   11   expensive
phone  grey   10   cheap

我写了太多方法来完成这个任务。最后我认为这段代码可以解决我的问题，但事实并非如此。我用 While 循环尝试了同样的事情，但我一直收到 Key Error 0。我该怎么办？这是我试过的代码：

df["D"] = "cheap"
A.values = df.A.unique()
for b in A.values:
    for i in range(len(df.loc[data.A== b])):
        if df.loc[df.A== b, "C"][i] >= df.loc[df.A== b, "C"].mean():
            df.loc[df.A== b, "D"][i] = "expensive"

Answer 1

用mean勾选transform，然后np.where

s = df.groupby('A').C.transform('mean')
df['D'] = np.where(df.C>s, 'expensive', 'cheap')
df
Out[158]: 
       A     B   C          D
0    car   red  15      cheap
1    car  blue  20  expensive
2    car  grey  14      cheap
3   bike   red   6      cheap
4   bike  blue   8  expensive
5  phone   red   9      cheap
6  phone  blue  11  expensive
7  phone  grey  10      cheap

Answer 2

df['D']=np.where(df[['A', 'B', 'C']].groupby('A').apply(lambda x: (x['C'].mean()>=x['C'])),'cheap','expensive')


     A     B   C          D
0    car   red  15      cheap
1    car  blue  20  expensive
2    car  grey  14      cheap
3   bike   red   6  expensive
4   bike  blue   8      cheap
5  phone   red   9      cheap
6  phone  blue  11  expensive
7  phone  grey  10      cheap

工作原理

np.where(condition, if met answer, not met answer)


#Apply boolean select to get condition. In this statement we seek to return true if mean is greater than price

condition= df[['A', 'B', 'C']].groupby('A').apply(lambda x: (x['C'].mean()>=x['C']))


if met answer= 'cheap'

not me t answer='expensive'

添加基于其他列和行的新列

Adding a new column based on other columns and rows

python

numpy

dataframe

pandas

keyerror