如何在 2 个不同的列 df 中获得具有共同值的 df？

Question

I have 2 dfs with different len:

   df1: 

        ESTACION    DZ
   0    ALAMOR      1
   1    EL TIGRE    1
   2    SAN PEDRO   1
   3    TABACONAS   1
   4    BATAN       2
   5    CACAO       2
   6    CHOTANO     2
   7    CIRATO      2
   8    LLAUCANO    2
   9    NARANJOS    2
   10   MAGUNCHAL   2
   11   PUCHACA     2
   12   MAYGASBAMBA 2

   df2:
        Estacion        Co  Dre
  56    ALAMOR          C   1
  89    LAGARTERA       C   1
  90    PUENTE PIURA    C   1
 211    PUENTE SULLANA  C   1
 249    PALTASHACO      C   1
 250    TAMBO GRANDE    C   1
 342    VENTANILLAS     C   2
 421    CACAO           C   2
 466    DESAGUADERO     C   2
 508    QUEBRADA HONDA  C   2

I want to save in another df (df3) common values between df1['ESTACION'] and df2['Estacion'] so i tried this code:

duplicates = pd.concat([df1,df2])[pd.concat([df1,df2])
                             .duplicated(subset=['ESTACION','Estacion'], keep=False)]

But i'm not getting the common values. I hope you can give me an answer or some advice. Thanks!!

Answer 1

Edited to make the answer more specific to your situation

You can use merge which by default does an inner join. And if you insist in having a dataframe with strictly the common values of a single column, try this:

df3=pd.merge(df1, df2, left_on=['ESTACION'], right_on=['Estacion'])
df3.drop(df3.columns.difference(['ESTACION']), 1, inplace=True)

Answer 2

If you want to get the common values regardless of where they appear and how many times, you can simply do this:

common_values = list(set(np.unique(df1['ESTACION'].values)).intersection(set(np.unique(df2['Estacion'].values))))

You need to have ran previously import numpy as np of course.

This will give you a list of all the values which are found in both columns. Then, you can assign them to a new DataFrame's column like so df3['common_values'] = common_values or do whatever else you want with those values.

Answer 3

I think you want to merge the dataframes, like this:

df3 = pd.merge(df1,df2, left_on=['ESTACION'],right_on=['Estacion'])

as mentioned in the comments, the column ESTACION is not the same as Estacion