Locate Pandas Series Values in Another Pandas Dataframe

Question

I have a Pandas Dataframe as follows:

dic = {
    'Name': ['Alsa','Jaguar','Lima','Panda','Foxtron','India'],
    'x'   : ['2233','1200'  ,'0341','2332' ,'1321'   ,'0445' ],
    'y'   : ['321' ,'231'   ,'440' ,'210'  ,'110'    ,'500'  ]
}

df = pd.DataFrame(dic)
df
    Name     x       y
0   Alsa    2233    321
1   Jaguar  1200    231
2   Lima    0341    440
3   Panda   2332    210
4   Foxtron 1321    110
5   India   0445    500

I have another Pandas Series as follows:

s
0       Alsa
0     Jaguar
0      Panda
0      India
1      Panda
1    Foxtron
2      India
2     Jaguar
2       Alsa

I need to locate the Pandas Series (s) in the Pandas Dataframe (df) and return the corresponding x and y. The expected result would be:

0       Alsa    2233    321
0     Jaguar    1200    231
0      Panda    2332    210
0      India    0445    500
1      Panda    2332    210
1    Foxtron    1321    110
2      India    0445    500
2     Jaguar    1200    231
2       Alsa    2233    321

One method is to use apply. However the data that I have is really huge and apply will be very slow. I'm looking for other solutions.

Note: Pandas Series include repeated values and I require them to be repeated, e.g. Alsa is repeated twice.

Convert `Series` to one column `DataFrame` and then use `merge` like `df = s.to_frame('Name').merge(df, how='left')` — jezrael, Mar 03 '21 at 10:10
If you see The Pandas series, the values are repeating more than once, e.g. Alsa is repeated twice. So would merge work in this case? — Ali Sultan, Mar 03 '21 at 10:12
With merge the indexes are reset. Is there a way to preserve the indexes of the series? — Ali Sultan, Mar 03 '21 at 11:33
Not easy, but possible `df = pd.DataFrame({'Name': s, 'idx':s.index}).merge(df, how='left').set_index('idx')` — jezrael, Mar 03 '21 at 11:34

Locate Pandas Series Values in Another Pandas Dataframe

0 Answers0