python - 如何更好地格式化我试图从多个回归中保存的输出？

Question

我想循环遍历线性回归的几个规范，并将每个模型的结果保存在 python 字典中。下面的代码有些成功，但字典中包含附加文本(例如数据类型信息)，使其不可读。此外，关于置信区间，我希望有两列单独的列 - 一列用于上限，另一列用于下限 - 但我无法做到这一点。

代码:

import patsy
import statsmodels.api as sm
from collections import defaultdict

colleges = ['ARC_g',u'CCSF_g',u'DAC_g',u'DVC_g',u'LC_g',u'NVC_g',u'SAC_g', u'SRJC_g',u'SC_g',u'SCC_g']
results = defaultdict(lambda: defaultdict(int))

for exog in colleges:
    exog = exog.encode('ascii')
    f1 = 'GRADE_PT_103 ~ %s -1' % exog
    y,X = patsy.dmatrices(f1, data,return_type='dataframe')
    mod = sm.OLS(y, X)    # Describe model

    res = mod.fit()       # Fit model

    results[exog]['beta'] = res.params  
#I'd like the confidence interval to be separated into two columns ('upper' and 'lower')
    results[exog]['CI'] = res.conf_int()
    results[exog]['rsq'] = res.rsquared

pd.DataFrame(results)

______电流输出

          ARC_g                      |   CCSF_g                        |  ...
beta  | ARC_g 0.79304 dtype: float64 |  CCSF_g 0.833644 dtype: float64
CI    | 0 1 ARC_g 0.557422 1.0... 0 1|   CCSF_g 0.655746 1...

rsq | 0.122551 | 0.213053

Answer 1

这就是我对您所展示内容的总结。希望它能给您带来一些想法。

import pandas as pd
import statsmodels.formula.api as smf

data = pd.DataFrame(np.random.randn(30, 5), columns=list('YABCD'))

results = {}
for c in data.columns[1:]:
    f = 'Y ~ {}'.format(c)
    r = smf.ols(formula=f, data=data).fit()
    coef = pd.concat([r.params,
                      r.conf_int().iloc[:, 0],
                      r.conf_int().iloc[:, 1]], axis=1, keys=['coef', 'lower', 'upper'])
    coef.index = ['Intercept', 'Beta']
    results[c] = dict(coef=coef, rsq=r.rsquared)


keys = data.columns[1:]
summary = pd.concat([results[k]['coef'].stack() for k in keys], axis=1, keys=keys)
summary.index = summary.index.to_series().str.join(' - ')
summary.append(pd.Series([results[k]['rsq'] for k in keys], keys, name='R Squared'))