DataFrame 中的 Python 数字格式

问：

我正在整理一个 p 值表（在下面的代码中var_sig），但我无法让数字以小数点后 4 位显示。我尝试了映射函数，但它返回了错误消息“未知格式代码'f'类型为'str'类型的对象。然后我尝试将 pval 包装在 float（） 中，但这也返回了一个错误。如何修复以下代码？

insdf = pd.DataFrame({'n':[500,1200,100,400,500,300],
                     'c':[42, 37, 1, 101, 73, 14],
                     'car':['small','medium','large','small','medium','large'],
                     'age':[1,1,1,2,2,2]})
insdf['n_log']=np.log(insdf['n'])
ratingvar = ['car','age']
def glm_freq(df, family, ratingvar, exposure, response, offset):  
    h2o.no_progress()
    hf = h2o.H2OFrame(df)    
    # first convert to categorical variables
    print('***Rebasing levels***')
    for r in ratingvar:
        hf[r] = hf[r].asfactor()
    # then re-basing on level with most exposure
        rebase = pd.pivot_table(df, 
                                values=[exposure], 
                                index=[r], 
                                aggfunc=np.sum).sort_values((exposure), ascending=False).index.to_list()
        rebase_list = [str(x) for x in rebase]
        hf[r] = hf[r].set_levels(levels=rebase_list)

    print('***Fitting GLM***')
    glm_model = H2OGeneralizedLinearEstimator(family= family,
                                              lambda_ = 0,
                                              compute_p_values = True,
                                              offset_column = offset
                                             )
    glm_model.train(ratingvar, response, training_frame= hf)
    print('***Printing model diagnostics***')
    print('Model AIC is: ', glm_model.aic())
    print('Model Residual Deviance is: ', glm_model.residual_deviance())
    llf = glm_model.scoring_history()['negative_log_likelihood'].iloc[-1]
    print('Log Likelihood is: ', llf)
    
    print('***Calculating Type III Statistics***')
    var_sig=pd.DataFrame(columns=['ratingvar','pval'])    
    for p in ratingvar:
        remainder_pred = ratingvar.copy()
        remainder_pred.remove(p)
        glm_model_tmp = H2OGeneralizedLinearEstimator(family= family,
                                              lambda_ = 0,
                                              compute_p_values = True, 
                                              offset_column = offset
                                             )        
        glm_model_tmp.train(remainder_pred, response, training_frame = hf)    
        llvar = glm_model_tmp.scoring_history()['negative_log_likelihood'].iloc[-1]            
        d_freedom = df[p].unique().shape[0] - 1
        print('Degree of freedom of ', str(p), ' is ', d_freedom)
        chisqstat = 2 * (llvar - llf)        
        print('Log Likelihood of model without ', str(p), ' is ', llvar)
        print('Chi-sq stat of ', str(p), ' is ', chisqstat)        
        pval = 1-stats.chi2.cdf(chisqstat, d_freedom)
        var_sig = var_sig.append({'ratingvar':p,'pval':pval}, ignore_index=True)
        var_sig['pval'] = var_sig['pval'].map('{:.4f}'.format)

    return var_sig
glm_freq(insdf, 'poisson', ratingvar, 'n', 'c', 'n_log')