[英]Cross join of three dataframes
I would like to join three dataframes of the following structure:我想加入以下结构的三个数据框:
january_df=pd.DataFrame({
'January':[4,4,3,2,1,1],
'Product_no':['B1','B2','S1','S2','B3','T1'],
'Label':['Ball','Bikini','Shoe','Shirt','Bag','Towel'],
'ID':[1000, 1001, 1002, 1003, 1004, 1005],
})
february_df=pd.DataFrame({
'February':[4,3,3,2,1,1],
'Product_no':['S1','B2','B1','T1','S2','B3'],
'Label':['Shoe','Bikini','Ball','Towel','Shirt','Bag'],
'ID':[1002, 1001, 1000, 1005, 1003, 1004],
})
march_df=pd.DataFrame({
'March':[5,1,1,1,1,1],
'Product_no':['T1','E1','S1','B3','L1','B1'],
'Label':['Towel','Earring','Shoe','Bag','Lotion','Ball'],
'ID':[1005, 1006, 1002, 1004, 1007, 1000],
})
The desired output for March should be:三月所需的 output 应该是:
January February March Product_no Label ID
---------------------------------------------------------
01 1 2 5 T1 Towel 1005
02 0 0 1 E1 Earring 1006
03 3 4 1 S1 Shoe 1002
04 1 1 1 B3 Bag 1004
05 0 0 1 L1 Lotion 1006
06 4 3 1 B1 Ball 1000
In a first step I tried to merge March and February第一步,我尝试合并三月和二月
all_df = pd.merge(march_df, february_df, on="ID")
but it does not yield the result for the two months.但它并没有产生两个月的结果。 I tried to understand the hints on Performant cartesian product (CROSS JOIN) with pandas and pandas three-way joining multiple dataframes on columns but did not get any wiser.
我试图用 pandas 和pandas 三路连接列上的多个数据帧来理解有关高性能笛卡尔积(CROSS JOIN)的提示,但没有得到任何更明智的结果。
In R it can be achieved as a "piped multiple join"在 R 中,它可以实现为“管道多重连接”
threeMonths <- February%>%
right_join(March)%>%
left_join(January)
which I cannot seem to translate into Python.我似乎无法翻译成 Python。
How do I get the output as wanted?如何获得所需的 output?
You can merge in two steps.您可以分两步合并。 For example for March:
以三月为例:
tmp = pd.merge(january_df, february_df, on='ID')
final_df = pd.merge(tmp, march_df, on='ID', how='right')[['January', 'February', 'March', 'Product_no', 'Label', 'ID']].fillna(0)
print(final_df)
Prints:印刷:
January February March Product_no Label ID
0 1.0 2.0 5 T1 Towel 1005
1 0.0 0.0 1 E1 Earring 1006
2 3.0 4.0 1 S1 Shoe 1002
3 1.0 1.0 1 B3 Bag 1004
4 0.0 0.0 1 L1 Lotion 1007
5 4.0 3.0 1 B1 Ball 1000
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