加入复杂的熊猫表

问题描述 投票:0回答:1

我正在尝试将statsmodels GLM的结果数据框加入到数据框中,该数据框用于在迭代模型时保存单变量数据和模型结果。我无法弄清楚如何语法加入这两个数据集。

我已经查阅了下面发现的熊猫文档,但没有运气:

https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging

这很困难,因为模型的输出与最终表相比较,最终表保存每个唯一变量的每个唯一级别的值。

使用以下代码查看数据的示例:

import pandas as pd

df = {'variable': ['CLded_model','CLded_model','CLded_model','CLded_model','CLded_model','CLded_model','CLded_model'
                  ,'channel_model','channel_model','channel_model']
      , 'level': [0,100,200,250,500,750,1000, 'DIR', 'EA', 'IA']
      ,'value': [460955.7793,955735.0532,586308.4028,12216916.67,48401773.87,1477842.472,14587994.92,10493740.36
               ,36388470.44,31805316.37]}

final_table = pd.DataFrame(df)


df2 = {'variable': ['intercept','C(channel_model)[T.EA]','C(channel_model)[T.IA]', 'CLded_model']
       , 'coefficient': [-2.36E-14,-0.091195797,-0.244225888, 0.00174356]}

model_results = pd.DataFrame(df2)

运行此操作后,您可以看到,对于分类变量,与final_table相比,该值包含在几个层中。像CLded_model这样的数值需要与它所关联的一个系数相结合。

这有很多,我不知道从哪里开始。

更新:以下代码生成所需的结果:

d3 = {'variable': ['intercept', 'CLded_model','CLded_model','CLded_model','CLded_model','CLded_model','CLded_model'
                   ,'CLded_model','channel_model','channel_model','channel_model']
      , 'level': [None, 0,100,200,250,500,750,1000, 'DIR', 'EA', 'IA']
      ,'value': [None, 60955.7793,955735.0532,586308.4028,12216916.67,48401773.87,1477842.472,14587994.92,10493740.36
               ,36388470.44,31805316.37]
      , 'coefficient': [ -2.36E-14, 0.00174356,  0.00174356,  0.00174356,  0.00174356,  0.00174356 ,0.00174356
                        , 0.00174356,None, -0.091195797,-0.244225888, ]}

desired_result = pd.DataFrame(d3)
pandas join
1个回答
1
投票

首先你必须清理df2:

df2['variable'] = df2['variable'].str.replace("C\(","")\
                                 .str.replace("\)\[T.", "-")\
                                 .str.strip("\]")

df2


       variable          coefficient
0   intercept           -2.360000e-14
1   channel_model-EA    -9.119580e-02
2   channel_model-IA    -2.442259e-01
3   CLded_model          1.743560e-03

因为你想要在级别列上合并一些df1而不是其他的,我们需要稍微更改df1以匹配df2:

df1.loc[df1['variable'] == 'channel_model', 'variable'] = "channel_model-"+df1.loc[df1['variable'] == 'channel_model', 'level']

df1

#snippet of what changed
      variable         level     value
6   CLded_model        1000   1.458799e+07
7   channel_model-DIR   DIR   1.049374e+07
8   channel_model-EA    EA    3.638847e+07
9   channel_model-IA    IA    3.180532e+07

然后我们合并它们:

df4 = df1.merge(df2, how = 'outer', left_on =['variable'], right_on = ['variable'])

我们得到你的结果(变量名中的微小变化除外)

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