pandas groupby从另一个数据帧汇总结果

我在使用groupby和aggregation时遇到问题，因此在使用多个数据帧时工作。我试图从两个不同的数据帧中计算num_maint_over_$90。pandas groupby从另一个数据帧汇总结果

cars_dict = {"ABC123": ["Ford", "Compact_Car"], "XYZ148": ["Chevy", "Truck"], "ASX133": ["Ford", "Truck"], "ADS111": ["Porsche", "Sports_Car"], "SSC119": ["Toyota", "Compact_Car"]} 
cars = pd.DataFrame.from_dict(cars_dict, orient = 'index') 
cars.columns = ["Manufacturer", "Type"] 
cars.index.rename("License_Plate", inplace = True) 

maintenance_dict = {"License_Plate": ["ABC123", "ABC123", "ABC123", "XYZ148", "ASX133", "ASX133", "ADS111", "ADS111", "SSC119"], "Cost": [60, 100, 200, 150, 40, 199, 33, 99, 0]} 
maintenance_records = pd.DataFrame.from_dict(maintenance_dict) 
maintenance_records.index.rename("order_num", inplace = True)

*汽车：*

   Manufacturer   Type 
License_Plate 
XYZ148    Chevy  Truck 
SSC119    Toyota Compact_Car 
ASX133    Ford  Truck 
ABC123    Ford Compact_Car 
ADS111    Porsche Sports_Car

* maintenance_records：*

  Cost License_Plate 
order_num 
0   60  ABC123 
1   100  ABC123 
2   200  ABC123 
3   150  XYZ148 
4   40  ASX133 
5   199  ASX133 
6   33  ADS111 
7   99  ADS111 
8    0  SSC119

*所需DF：*

Type   num_maint_over_$90 
Compact_Car 2 
Sports_Car 1 
Truck  2

我试过使用groupby，apply()和for loop。

来源

2016-06-29 Jason Brown

merged = pd.merge(maintenance_records, cars, how='left', 
        left_on='License_Plate', right_index=True) 
merged.query('Cost > 90')['Type'].value_counts()

来源

2016-06-29 17:11:06

的'maintenance_dict'在你的问题是缺少'Cost'列，顺便一提。 –

我希望除了合并表格之外还有一种方法。这工作，谢谢。 –

@JBr如果此答案已解决您的问题，请点击复选标记考虑[接受它]（http://meta.stackexchange.com/q/5234/179419）。这向更广泛的社区表明，您已经找到了解决方案，并为答复者和您自己提供了一些声誉。没有义务这样做。 –

这里是天真的for循环解决方案：

car_types = {} 

for index, row in cars.iterrows(): 
    car_type = row["Type"] 

    if car_type not in car_types: 
     car_types[car_type] = 0 

for index, row in maintenance_records.iterrows(): 

    if row["Cost"] > 90: 
     car_license = row["License_Plate"] 
     car_type = cars.loc[car_license,"Type"] 
     car_types[car_type] += 1 

df = pd.DataFrame.from_dict(car_types, orient = "index") 
df.index.rename("Type", inplace = True) 
df.columns = ["num_maint_over_$90"]

* DF：*

   num_maint_over_$90 
Type 
Sports_Car     1 
Compact_Car     2 
Truck       2

来源

2016-06-29 17:24:53

pandas groupby从另一个数据帧汇总结果

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