Python pandas 使用带有自定义 agg 函数的 groupby 创建新列

Question

我的数据框:

from random import random, randint
from pandas import DataFrame

t = DataFrame({"metasearch":["A","B","A","B","A","B","A","B"],
                   "market":["A","B","A","B","A","B","A","B"],
                   "bid":[random() for i in range(8)],
                   "clicks": [randint(0,10) for i in range(8)],
                   "country_code":["A","A","A","A","A","B","A","B"]})

我想为每个 market 拟合 LinearRegression，所以我:

1) 组 df - groups = t.groupby(by="market")

2) 准备函数以在组上拟合模型 -

from sklearn.linear_model import LinearRegression
def group_fitter(group):
    lr = LinearRegression()
    X = group["bid"].fillna(0).values.reshape(-1,1)
    y = group["clicks"].fillna(0)
    lr.fit(X, y)
    return lr.coef_[0] # THIS IS A SCALAR

3) 创建一个以market为索引，以coef为值的新系列:

s = groups.transform(group_fitter) 

但是第 3 步失败了: KeyError: ('bid_cpc', 'occurred at index bid')

Answer 1

我认为您需要使用 transform 而不是 apply，因为在函数中同时使用更多列，对于新列使用 join :

from sklearn.linear_model import LinearRegression
def group_fitter(group):
    lr = LinearRegression()
    X = group["bid"].fillna(0).values.reshape(-1,1)
    y = group["clicks"].fillna(0)
    lr.fit(X, y)
    return lr.coef_[0] # THIS IS A SCALAR

groups = t.groupby(by="market")
df = t.join(groups.apply(group_fitter).rename('new'), on='market')
print (df) 
        bid  clicks country_code market metasearch       new
0  0.462734       9            A      A          A -8.632301
1  0.438869       5            A      B          B  6.690289
2  0.047160       9            A      A          A -8.632301
3  0.644263       0            A      B          B  6.690289
4  0.579040       0            A      A          A -8.632301
5  0.820389       6            B      B          B  6.690289
6  0.112341       5            A      A          A -8.632301
7  0.432502       0            B      B          B  6.690289