python - 如何使用 Python pandas Df 将 csvs 与超过 1 个相同的列合并并仅添加不同的列

Question

这道题类似于简单的mysql操作-

UPDATE hpaai_month_div t, fahafa_monthly s SET t.col1=s.col1 WHERE t.col2=s.col2 AND t.year=s.year AND t.month=s.month;

Data :

CSV Amonth    year   col2      col1abc      2000   DEFSSDS   190def      2001   GHISFDS   210ghi      2002   SJDYHGF   910CSV Bmonth   year    col2     col1    stat_fipsabc     2000    DEFSSDS   0        adef     2001    GHISFDS   0        bghi     2002    SJDYHGF   0        cResulting CSV :month    year   col2      col1    stat_fipsabc     2000    DEFSSDS   190       adef     2001    GHISFDS   210       bghi     2002    SJDYHGF   910       c

Code so far : (not working as desired)

   df_a = pd.read_csv('a.csv')
   df_b = pd.read_csv('b.csv')
   merged_df = pd.merge(df_a, df_b, on="col1", how="left")

   merged_df = pd.concat([merged_df], axis=1)
   merged_df.to_csv('final_output.csv', encoding='utf-8', index=False)
   print open('final_output.csv').read()

如何获取数据作为结果csv

Answer 1

看来你需要merge , 最后删除列 col_:

#default inner join 
df = pd.merge(df1, df2, on=['col2','year','month'], suffixes=('','_'))
       .drop('col1_',axis=1)
print (df)
  month  year     col2  col1 stat_fips
0   abc  2000  DEFSSDS   190         a
1   def  2001  GHISFDS   210         b
2   ghi  2002  SJDYHGF   910         c

---

df = pd.merge(df1, df2, on=['col2','year','month'])
print (df)
  month  year     col2  col1_x  col1_y stat_fips
0   abc  2000  DEFSSDS     190       0         a
1   def  2001  GHISFDS     210       0         b
2   ghi  2002  SJDYHGF     910       0         c


df = pd.merge(df1, df2, on=['col2','year','month'], suffixes=('','_'))
print (df)
  month  year     col2  col1  col1_ stat_fips
0   abc  2000  DEFSSDS   190      0         a
1   def  2001  GHISFDS   210      0         b
2   ghi  2002  SJDYHGF   910      0         c