在 Pandas 数据框中应用函数或 for 循环来替换空值的最佳方法是什么？

如何解决在 Pandas 数据框中应用函数或 for 循环来替换空值的最佳方法是什么？

我有一个包含 100000 条记录的数据集，我需要根据多列替换空值。
我尝试了两种方法：

#First Approach    
# Missing value treatment
    start_time = time.time()
    
    data['date_of_last_rech_data_6'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_6','total_rech_data_6','max_rech_data_6']))) else x['date_of_last_rech_data_6'],axis = 1)
    data['total_rech_data_6'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_6','max_rech_data_6']))) else x['total_rech_data_6'],axis = 1)
    data['max_rech_data_6'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_6','max_rech_data_6']))) else x['max_rech_data_6'],axis = 1)
    
    data['date_of_last_rech_data_7'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_7','total_rech_data_7','max_rech_data_7']))) else x['date_of_last_rech_data_7'],axis = 1)
    data['total_rech_data_7'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_7','max_rech_data_7']))) else x['total_rech_data_7'],axis = 1)
    data['max_rech_data_7'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_7','max_rech_data_7']))) else x['max_rech_data_7'],axis = 1)
    
    data['date_of_last_rech_data_8'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_8','total_rech_data_8','max_rech_data_8']))) else x['date_of_last_rech_data_8'],axis = 1)
    data['total_rech_data_8'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_8','max_rech_data_8']))) else x['total_rech_data_8'],axis = 1)
    data['max_rech_data_8'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_8','max_rech_data_8']))) else x['max_rech_data_8'],axis = 1)
    
    data['date_of_last_rech_data_9'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_9','total_rech_data_9','max_rech_data_9']))) else x['date_of_last_rech_data_9'],axis = 1)
    data['total_rech_data_9'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_9','max_rech_data_9']))) else x['total_rech_data_9'],axis = 1)
    data['max_rech_data_9'] = data.apply(lambda x: 0 if(np.all(pd.isnull(['date_of_last_rech_data_9','max_rech_data_9']))) else x['max_rech_data_9'],axis = 1)
    
    end_time = time.time()
    print(end_time-start_time)

这段代码花费的时间是 152.52092480659485。

#Second Approach
start_time = time.time()

for i in range(0,len(data)):
    # Missing value treatment for the month of June
    if pd.isnull((data['date_of_last_rech_data_6'][i]) and (data['total_rech_data_6'][i]) and (data['max_rech_data_6'][i]) ):
        data['date_of_last_rech_data_6'][i]=0
        data['total_rech_data_6'][i]=0
        data['max_rech_data_6'][i]=0 
    # Missing value treatment for the month of July
    if pd.isnull((data['date_of_last_rech_data_7'][i]) and (data['total_rech_data_7'][i]) and (data['max_rech_data_7'][i]) ):
        data['date_of_last_rech_data_7'][i]=0
        data['total_rech_data_7'][i]=0
        data['max_rech_data_7'][i]=0
    # Missing value treatment for the month of August
    if pd.isnull((data['date_of_last_rech_data_8'][i]) and (data['total_rech_data_8'][i]) and (data['max_rech_data_8'][i]) ):
        data['date_of_last_rech_data_8'][i]=0
        data['total_rech_data_8'][i]=0
        data['max_rech_data_8'][i]=0 
    # Missing value treatment for the month of September
    if pd.isnull((data['date_of_last_rech_data_9'][i]) and (data['total_rech_data_9'][i]) and (data['max_rech_data_9'][i]) ):
        data['date_of_last_rech_data_9'][i]=0
        data['total_rech_data_9'][i]=0
        data['max_rech_data_9'][i]=0  
        
end_time = time.time()
print(end_time-start_time)

此代码花费的时间 223.60794281959534。但是这段代码有时会运行，有时会挂起并停止内核。

是否有其他最佳方法可以做到这一点？