How to merge csv files with different headers but same data on condition

Use:

print (merged_data)
    id    user  product  price[78]  price[79]  Source
0  105  dummya      egg         22       28.0  sheet1
1  119  dummy1     soya         67        NaN  sheet1
2  567  dummya  spinach         22       28.0  sheet2
3  897  dummy1     rose         67       99.0  sheet2
4  345  dummya      egg         87       98.0  sheet3
5  121  dummy1   potato         98       99.0  sheet3

print (Condition)
    Sheet   ID  price1_col1  price1_col2                    price1_out  \
0  sheet1  yes           78          NaN                   price1_col1   
1  sheet2  yes           78         79.0       price1_col1+price1_col2   
2  sheet3  yes           78         79.0  max(price1_col1,price1_col2)   

   price2_col1  price2_col2                    price2_out sheetname  
0           78          NaN                   price2_col1       yes  
1           78         79.0       price2_col1+price2_col2       yes  
2           79         78.0  min(price2_col1,price2_col2)        no  

---

#merge data together by left join    
df = merged_data.merge(Condition.rename(columns={'Sheet':'Source'}), on='Source', how='left')
#replace columns to empty strings, remove sheetname and ID columns
df['Source'] = np.where(df.pop('sheetname') == 'yes', df['Source'], '')
df['id'] = np.where(df.pop('ID') == 'yes', df['id'], '')

#filter integers between [] to ned DataFrame 
df1 = df.filter(regex='\[\d+\]').copy()
#filter all columns with price, exclude df1 
df2 = df[df.filter(regex='price').columns.difference(df1.columns)].copy()
#convert column to integers
df1.columns = df1.columns.str.extract('\[(\d+)\]', expand=False).astype(int)
#helper column for match missing values
df1['a'] = np.nan
#filter columns without/with _out
mask = df2.columns.str.endswith(('_col1','_col2'))
final_cols = df2.columns[ ~mask]
removed_cols = df2.columns[mask]
#replace columns by match values from df2
for c in removed_cols:
    df2[c] = df1.lookup(df1.index, df2[c].fillna('a'))

---

print (df2)

   price1_col1  price1_col2                    price1_out  price2_col1  \
0           22          NaN                   price1_col1         22.0   
1           67          NaN                   price1_col1         67.0   
2           22         28.0       price1_col1+price1_col2         22.0   
3           67         99.0       price1_col1+price1_col2         67.0   
4           87         98.0  max(price1_col1,price1_col2)         98.0   
5           98         99.0  max(price1_col1,price1_col2)         99.0   

   price2_col2                    price2_out  
0          NaN                   price2_col1  
1          NaN                   price2_col1  
2         28.0       price2_col1+price2_col2  
3         99.0       price2_col1+price2_col2  
4         87.0  min(price2_col1,price2_col2)  
5         98.0  min(price2_col1,price2_col2)

---

#create MultiIndex for separate eah price groups
df2.columns = df2.columns.str.split('_', expand=True)

def f(x):
    #remove first level
    x.columns = x.columns.droplevel(0)
    out = []
    #loop each row
    for v in x.itertuples(index=False):
        #remove prefix
        t = v.out.replace(x.name+'_', '')
        #loop each namedtuple and replace values
        for k1, v1 in v._asdict().items():
            t = t.replace(k1, str(v1))
        #pd.eval cannot working with min, max, so handled different
        if t.startswith('min'):
            out.append(min(pd.eval(t[3:])))
        elif t.startswith('max'):
            out.append(max(pd.eval(t[3:])))
        #handled +-*/
        else:
            out.append(pd.eval(t))
    #return back
    return pd.Series(out)

#overwrite original columns
df[final_cols] = df2.groupby(level=0, axis=1).apply(f).add_suffix('_out')
#if necessary remove helpers
df = df.drop(removed_cols, axis=1)

---

print (df)
    id    user  product  price[78]  price[79]  Source  price1_out  price2_out
0  105  dummya      egg         22       28.0  sheet1        22.0        22.0
1  119  dummy1     soya         67        NaN  sheet1        67.0        67.0
2  567  dummya  spinach         22       28.0  sheet2        50.0        50.0
3  897  dummy1     rose         67       99.0  sheet2       166.0       166.0
4  345  dummya      egg         87       98.0                98.0        87.0
5  121  dummy1   potato         98       99.0                99.0        98.0