如何从不同的熊猫数据框中选择选定的列

Question

I have 3 pandas dataframes (similar to the below one). I have 2 lists list ID_1 = ['sdf', 'sdfsdf', ...] and list ID_2 = ['kjdf', 'kldfjs', ...]

Table1:
    ID_1    ID_2    Value
0   PUFPaY9 NdYWqAJ 0.002
1   Iu6AxdB qANhGcw 0.01
2   auESFwW jUEUNdw 0.2345
3   LWbYpca G3uZ_Rg 0.0835
4   8fApIAM mVHrayg 0.0295

Table2:
     ID_1    weight1 weight2 .....weightN
0   PUFPaY9     
1   Iu6AxdB     
2   auESFwW 
3   LWbYpca     

Table3:
    ID_2    weight1 weight2 .....weightN
0   PUFPaY9     
1   Iu6AxdB     
2   auESFwW     
3   LWbYpca     

I want to have one dataframe which should be calculated like,

for each x ID_1 in list1:
    for each y ID_2 in list2:
        if x-y exist in Table1:
            temp_row = ( x[weights[i]].* y[weights[i]])
            # here i want one to one multiplication, x[weight1]*y[weight1] , x[weight2]*y[weight2]
            temp_row.append(value[x-y] in Table1)
            new_dataframe.append(temp_row)

return new_dataframe

The required new_dataframe should look like Table4:

Table4:
        weight1 weight2 weight3 .....weightN value
    0           
    1           
    2       
    3       

What I am able to do now is:

new_df = df[(df.ID_1.isin(list1)) & (df.ID_2.isin(list2))] using this I am getting all valid ID_1 and ID_2 combination and values. But I have no idea, how I can get the multiplication of weights form both datafames ( without looping for each weight[i] )?

Now task is easier, I can iterate over the new_df and for each row in new_df , I will find weight[i to n] for ID_1 from table 2 and weight[i to n] for ID_2 from table3 . Then I can append their one-one multiplication with "value" from table1 to new FINAL_DF . But I don't want to loop and do, can we solve this using some smarter way?

Answer 1

is that what you want?

data = """\
ID_1
PUFPaY9     
aaaaaaa
Iu6AxdB     
auESFwW 
LWbYpca
"""
id1 = pd.read_csv(io.StringIO(data), delim_whitespace=True)

data = """\
ID_2   
PUFPaY9
Iu6AxdB
xxxxxxx
auESFwW
LWbYpca
"""
id2 = pd.read_csv(io.StringIO(data), delim_whitespace=True)

cols = ['weight{}'.format(i) for i in range(1,5)]
for c in cols:
    id1[c] = np.random.randint(1, 10, len(id1))
    id2[c] = np.random.randint(1, 10, len(id2))

id1.set_index('ID_1', inplace=True)
id2.set_index('ID_2', inplace=True)

df_mul = id1 * id2

Step by step:

In [215]: id1
Out[215]:
         weight1  weight2  weight3  weight4
ID_1
PUFPaY9        8        9        1        1
aaaaaaa        6        1        9        2
Iu6AxdB        8        4        8        5
auESFwW        9        3        4        2
LWbYpca        7        7        1        8

In [216]: id2
Out[216]:
         weight1  weight2  weight3  weight4
ID_2
PUFPaY9        6        5        5        1
Iu6AxdB        1        5        4        5
xxxxxxx        1        2        6        4
auESFwW        3        9        5        5
LWbYpca        3        3        6        7

In [217]: id1 * id2
Out[217]:
         weight1  weight2  weight3  weight4
Iu6AxdB      8.0     20.0     32.0     25.0
LWbYpca     21.0     21.0      6.0     56.0
PUFPaY9     48.0     45.0      5.0      1.0
aaaaaaa      NaN      NaN      NaN      NaN
auESFwW     27.0     27.0     20.0     10.0
xxxxxxx      NaN      NaN      NaN      NaN