如何使用熊猫中的for循环根据另一列的条件填充一列中的缺失值？

Question

weather_train=pd.DataFrame({
'site_id':[0,0,0,0,0,0,1,1,1,1,1],
'air_temperature': [25,22,'NaN',28,'NaN',30,45,'NaN',50,'Nan',24]
})

• When site_id is 0, I need to calculate the mean air_temperature for site_id 0 and then use the mean to fill in the missing values for air_temperature in site_id 0.
• Then, when the site_id is 1, I need to calculate the mean air_temperature for site_id 1 and fill in the missing values for air_temperature in site_id 1.

Have to do the same process for cloud_coverage .

Can anyone help me write a for loop in pandas for this?

Answer 1

No need for loops. Simply use groupby().transform() for inline mean aggregation enclosed in a conditional numpy.where :

weather_train['air_temperature'] = np.where(pd.isnull(weather_train['air_temperature']),
                                            weather_train.groupby(['site'])['air_temperature'].transform('mean'),    
                                            weather_train['air_temperature'])