Python 如何加入/合并 Pandas 数据帧与来自不同数据帧的特定值的匹配列

Question

I have two different datasets a and b. I want to left join b to a but I want to join to a where only left join b['ColA'] and b['ColC'] to matching a['ColA'] and a['ColC']==1

something like expected_table = pd.merge(a,b, left_on=['ColA', ['ColC']==1 ] ,rigth_on = ['ColA',['ColC']==0])

a =  pd.DataFrame({"ColA":["num 1", "num 2", "num 3"],
                   "ColB":[5,6,7],
                   "ColC":[1,1,0]})

b =  pd.DataFrame({"ColA":["num 1", "num 2", "num 4"],
                   "Colx":[10,16,71],
                   "Coly":[0,0,0]})

Coly is all equal 0

expected= pd.DataFrame({"ColA":["num 1", "num 2", "num 3"],
                   "ColB":[5,6,7],
                   "ColC":[1,1,0], 
                   "Colx":[10,16,None]})```

I solve it by creating a new column on b table that matches same value with a['colx'] .

But I wonder if there is a way to let you use conditions in merge/join process like in sql.

Answer 1

There is no feature in Pandas to directly use conditions in merge/join process like in sql. Anyway, we can simulate this by chaining the Pandas .merge() function and perform the filtering by .query() which has syntax like sql where condition syntax.

To do this, you can do a left join on a and b on matching ColA and set indicator=True for us to distinguish whether the merged row entry is from a only or from merging both a and b .

Then, use .query() to filter on the required condition that if merging from both, ColC == 1 and Coly == 0 . Otherwise, if only from a , we keep the row.

df_out = (pd.merge(a, b, left_on='ColA', right_on ='ColA', how='left', indicator=True)
            .query('(_merge == "left_only") | ((ColC == 1) & (Coly == 0))')
         )

Result:

print(df_out)


    ColA  ColB  ColC  Colx  Coly     _merge
0  num 1     5     1  10.0   0.0       both
1  num 2     6     1  16.0   0.0       both
2  num 3     7     0   NaN   NaN  left_only

Then, we can drop the unwanted columns by .drop , as follows:

df_out = df_out.drop(['Coly', '_merge'], axis=1)

Result:

print(df_out)

    ColA  ColB  ColC  Colx
0  num 1     5     1  10.0
1  num 2     6     1  16.0
2  num 3     7     0   NaN