如何加速python字典循环？

Question

我有一个数据帧和一个嵌套字典。我需要将字典中的键与dataframe列的名称匹配，并附加一个包含所述字典的正确值的列。最终的值是字典中的两个或三个深度。然而，这个循环已经运行了两天。我觉得我的循环效率不高。有谁能快速查看一下这个语法并找出任何低效之处吗？你知道吗

   for index, row in df.iterrows():
        for k1, v1 in model_4_factors.items():
            for k2, v2 in v1.items():
                if isinstance(v2, dict):
                    for k3, v3 in v2.items():
                        if k2 == row['RATING_CLASS_CODE'] and k3 == row['unit_value_model']:
                            df['value_factor_4'] = v3
                        elif k2 == row['RATING_CLASS_CODE'] and k3 == row['MVEH_CC_Model']:
                            df['cc_factor_4'] = v3
                        elif k2 == row['term_model'] and k3 == row['advanced_purchase_days_model']:
                            df['advanced_days_factor_4'] = v3
                        elif k2 == row['marital_status_model'] and k3 == row['Driver_Age_model']:
                            df['driver_age_factor_4'] = v3
                        else:
                            df['value_factor_4'] = None

                else:
                    if row['mveh_pkg_typ_cd'] == k2:
                        df['mveh_pkg_typ_cd_factor_4'] = v2

                    elif row['CLded'] == k2:
                        df['CLded_factor_4'] = v2

                    elif row['unit_drv_exp_model'] == k2:
                        df['unit_drv_exp_model'] = v2

                    elif row['v_age_model'] == k2:
                        df['v_age_model_factor_4'] = v2

                    elif row['years_owned_model'] == k2:
                        df['years_owned_model_factor_4'] = v2

                    elif row['FIN_RESP_CD'] == k2:
                        df['FIN_RESP_CD_factor_4'] = v2

                    elif row['st_ad_moto_safety_course'] == k2:
                        df['st_ad_moto_safety_course_factor_4'] = v2

                    elif row['st_ai_instructor_course'] == k2:
                        df['st_ai_instructor_course_factor_4'] = v2

                    elif row['st_ci_instructor_course'] == k2:
                        df['st_ci_instructor_course_factor_4'] = v2

                    elif row['st_mf_moto_safety_course '] == k2:
                        df['st_mf_moto_safety_course_factor_4'] = v2

                    elif row['st_mi_instructor_course'] == k2:
                        df['st_mi_instructor_course_factor_4'] = v2

                    elif row['st_mc_moto_safety_course'] == k2:
                        df['st_mc_moto_safety_course_factor_4'] = v2

                    elif row['st_rc_moto_safety_course'] == k2:
                        df['st_rc_moto_safety_course_factor_4'] = v2

                    elif row['st_ri_instructor_course'] == k2:
                        df['st_ri_instructor_course_factor_4'] = v2

                    elif row['ds_pn_prior_insurance'] == k2:
                        df['ds_pn_prior_insurance_factor_4'] = v2

                    elif row['st_rc_moto_safety_course'] == k2:
                        df['st_rc_moto_safety_course_factor_4'] = v2

                    elif row['Loyalty'] == k2:
                        df['Loyalty_factor_4'] = v2

                    elif row['ds_mc_motorcycle_endorsement'] == k2:
                        df['ds_mc_motorcycle_endorsement_factor_4'] = v2

                    elif row['multi_unit_model2'] == k2:
                        df['multi_unit_model2_factor_4'] = v2

                    elif row['ds_ad_affinity'] == k2:
                        df['ds_ad_affinity_factor_4'] = v2

                    elif row['ds_ak_alliance'] == k2:
                        df['ds_ak_alliance_factor_4'] = v2

                    elif row['multi_policy_count_model'] == k2:
                        df['multi_policy_count_model_factor_4'] = v2

                    elif row['ds_fp_paid_in_full'] == k2:
                        df['ds_fp_paid_in_full_factor_4'] = v2

                    else:
                        pass 

数据帧约为800000行。字典也是一个很大的代码块。样本来自下面的字典和DF。factors' is the dictionary I'm working with.df\u result`是我想要的结果减去列的顺序。你知道吗

import pandas as pd

vn_value = {23000: 1, 30000: 2, 26000: 3}
g_cn_value = {2000:1, 3000: 2, 4000: 3}
la_value = {19000: .5, 20000: 1, 20500: 1.2}
unit_value_factors = {'VN':vn_value, 'G_CN':g_cn_value, 'LA':la_value}
package = {'VPN':.3, 'SPS':.5, 'LSS':.7, 'SCE':.8}
d_cc = {1700: 1.3, 1800:2.1}
E_cc = {1300:1.8, 1400:.6}
HD_cc = {1400:1.5, 1800:1.7, 1900:1.8}
cc_factors = {'D':d_cc, 'E':E_cc, 'HD':HD_cc}

factors = {'value_class':unit_value_factors, 'package_modifier':package, 'cc_class':cc_factors}



df_value_class = ['VN', 'VN', 'G_CN', 'LA']
df_value = [26000, 30000, 3000, 19000]
df_package = ['VPN', 'SPS', 'LSS', 'SCE']
df_cc_class = ['D', 'E', 'HD', 'HD']
df_cc = [1700, 1300, 1400, 1900]

value_factor = [3,2,2,.5]
package_factor = [.3, .5 ,.7, .8]
cc_factor = [1.3, 1.8, 1.5, 1.8]

df1 = pd.DataFrame({'value_class':df_value_class, 'value':df_value, 'package_modifier':df_package
                   , 'cc_class':df_cc_class, 'cc':df_cc, 'AI':df_sc_AI, 'MS':df_sc_MS})


df_result = pd.DataFrame({'value_class':df_value_class, 'value':df_value, 'value_answer':value_factor
                          , 'package_modifier':df_package, 'package_answer':package_factor, 'cc_class':df_cc_class
                          , 'cc':df_cc, 'cc_answer':cc_factor})

df_result['score'] = df_result['value_answer']*df_result['package_answer'] * df_result['cc_answer']

Answer 1

这要快得多。你知道吗

for column in columns:
    for key, value in model_4_factors.items():
        if column == key:
            df[column+'_factor_4'] = df[key].map(value)