参数必须是字符串或数字，而不是“单元格”-不能解决问题

Question

下面有下面的代码。

我试图将'rankedvariableslist'中的R-平方值和P-值从OLS回归输出中存储，然后根据P值，然后根据R-平方值对数据进行排序。

但是，我得到了错误：'float()参数必须是字符串或数字，而不是'Cell''

我相信这可能是因为我的“R-平方”和“P-值”是'cell'类型的，我试图将它们转换为浮动/int，但没有成功。

如果能伸出援手，我将不胜感激！

correspondantsleepvariable = []
correspondantpvalue = []
correspondantpvalue = [] 

newerresults = resultmodeldistancevariation2sleepsummary.tables[0]
newerdata = pd.DataFrame(newerresults)
rsquaredvalue = newerdata.iloc[0,3]
rsquaredvalues.append(rsquaredvalue)
modelpvalues = resultmodeldistancevariation2sleepsummary.tables[1]
newerdatavalues = pd.DataFrame(modelpvalues)
pvalue = newerdatavalues.iloc[12,4]
correspondantpvalue.append(pvalue)
correspondantsleepvariable.append(sleepvariable[i])
rankedvariableslist = pd.DataFrame({'Sleepvariables':correspondantsleepvariable, 'R-squared value':rsquaredvalues,'P-value':correspondantpvalue})
listed = list(range(0, 21))
listed = pd.DataFrame(listed)
rankedvariableslist = pd.concat((rankedvariableslist,listed),axis=1)
rankedvariableslist = rankedvariableslist.rename(columns={0: "Value"})
rankedvariableslist['R-squared value'] = rankedvariableslist['R-squared value'].astype('category').cat.as_ordered()
rankedvariableslist['P-value'] = rankedvariableslist['P-value'].astype('category').cat.as_ordered()
rankedvariableslist['Sleepvariables'] = rankedvariableslist['Sleepvariables'].astype('category').cat.as_ordered()
rankedvariableslist.sort_values(['P-value','R-squared value'],ascending = [True, False])
print(rankedvariableslist.head(3)

                         Sleepvariables  R-squared value P-value
0                        hours_of_sleep           0.026   0.491
1              frequency_of_alarm_usage           0.026   0.681
2                        sleepiness_bed           0.026   0.413

As an example of the dataframe 'newerresults':

                            OLS Regression Results                            
==============================================================================
Dep. Variable:               distance   R-squared:                       0.028
Model:                            OLS   Adj. R-squared:                  0.016
Method:                 Least Squares   F-statistic:                     2.338
Date:                Fri, 18 Nov 2022   Prob (F-statistic):            0.00773
Time:                        12:39:29   Log-Likelihood:                -1274.1
No. Observations:                 907   AIC:                             2572.
Df Residuals:                     895   BIC:                             2630.
Df Model:                          11                                         
Covariance Type:            nonrobust                                         
==============================================================================

如果能伸出援手，我将不胜感激！

Answer 1

下面的代码起作用了--我没有将模型摘要输出转换为dataframe，而是将模型摘要输出转换为html文件)。

correspondantsleepvariable = []
correspondantpvalue = []
correspondantpvalue = [] 

results_as_html = resultmodeldistancevariation2sleepsummary.tables[0].as_html()
datehere = pd.read_html(results_as_html, header=None, index_col=None)[0]
rsquaredvalue = datehere.iloc[0,3]
rsquaredvalue.astype(float)
rsquaredvalues.append(rsquaredvalue)
results_as_html = resultmodeldistancevariation2sleepsummary.tables[1].as_html()
datehere = pd.read_html(results_as_html, header=0, index_col=0)[0]
pvalue = datehere.iloc[11,3]
pvalue.astype(float)
correspondantpvalue.append(pvalue)
correspondantsleepvariable.append(sleepvariable[i])
rankedvariableslist = 
pd.DataFrame({'Sleepvariables':correspondantsleepvariable, 'R-squared value':rsquaredvalues,'P-value':correspondantpvalue})
rankedvariableslist.sort_values(by=['P-value','R-squared value'],ascending = [True,False],inplace=True)
print(rankedvariableslist)

Sleepvariables  R-squared value  P-value
9    time_spent_awake_during_night_mins            0.034    0.005
4                         sleep_quality            0.030    0.041
20          sleepiness_resolution_index            0.028    0.129