OLS 回归与 groupby

Question

我想运行使用 pandas 和 groupby 进行 OLS 回归。

我正在尝试以下代码：

import pandas as pd
from pandas.stats.api import ols

df=pd.read_csv(r'F:\File.csv')
result=df.groupby(['FID']).apply(lambda x: ols(y=df[x['MEAN']], x=df[x['Accum_Prcp'],x['Accum_HDD']]))
print result

但是这个returns:

File "C:\Users\spotter\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 1150, in _convert_to_indexer
    raise KeyError('%s not in index' % objarr[mask])

    KeyError: '[ 0.84978328  0.72115778  0.53965104  0.52955655  0.73372541  0.64617074\n  0.60040938  0.7147218   0.65533535  0.57980322  0.57382068  0.56543435\n  0.70740831  0.9245337   0.54859569  0.6789395   0.7086157   0.3835853\n  0.54924104  0.80813778  0.83758118  0.22673391  0.26594087  0.63650468\n  0.89889911  0.38324657  0.30235986  0.62922678  0.55219822  0.55950705\n  0.71137557  0.53631811  0.70158798  0.87116361  0.93751381  0.91125518\n  0.80020908  0.75301262  0.82391046  0.77483673  0.63069573  0.44954455\n  0.83578862  0.56338649  0.64236039  0.93270243  0.93077291  0.83847668\n  0.8268959   0.85400317  0.74319769  0.94803537  0.97484929  0.45366017\n  0.80823694  0.82028051  0.63960395  0.63015722  0.73132888  0.55570184\n  0.83265402  0.75009687  0.58207032  0.92064804  0.91058008  0.86726397\n  0.89204098  0.95573514  0.75704367  0.80786363  0.87448548  0.7553715\n  0.88965962  0.82828493  0.82423891  0.81034742  0.90104876  0.78875473\n  0.97369268] not in index'

我的语法有什么不正确的地方吗？

在没有 groupby 的情况下执行此操作将是这样的：

result = ols(y=df['MEAN'], x=df[['Accum_HDD','Accum_Prcp']])

并且工作正常。

我的数据框看起来像这样：

FID  Image_Date   MEAN  Accum_Prcp   Accum_HDD
1     19920506     2.0   500.0        1000.0
1     19930506     1.7   450.0        1050.0
2     19920506     2.7   456.0        992.0
2     19930506     1.9   376.0        800.0

Answer 1

尝试：

grps=df.groupby(['FID'])
for fid, grp in grps:
    ols(y=grp.loc[:, 'MEAN'], x=grp.loc[:, ['Accum_Prcp', 'Accum_HDD']])

OLS 回归与 groupby

OLS Regression with groupby

python

pandas

statsmodels