How to form tuple column from two columns in Pandas

Pandas has the itertuples method to do exactly this:

list(df[['lat', 'long']].itertuples(index=False, name=None))

Get comfortable with zip. It comes in handy when dealing with column data.

df['new_col'] = list(zip(df.lat, df.long))

It's less complicated and faster than using apply or map. Something like np.dstack is twice as fast as zip, but wouldn't give you tuples.

You should try using pd.to_records(index=False):

import pandas as pd
df = pd.DataFrame({'language': ['en', 'ar', 'es'], 'greeting': ['Hi', 'اهلا', 'Hola']})
df

   language  greeting
0       en    Hi
1       ar    اهلا
2       es   Hola

df['list_of_tuples'] = list(df[['language', 'greeting']].to_records(index=False))
df['list_of_tuples']

0    [en, Hi]
1    [ar, اهلا]
2    [es, Hola]

enjoy!

In [10]: df
Out[10]:
          A         B       lat      long
0  1.428987  0.614405  0.484370 -0.628298
1 -0.485747  0.275096  0.497116  1.047605
2  0.822527  0.340689  2.120676 -2.436831
3  0.384719 -0.042070  1.426703 -0.634355
4 -0.937442  2.520756 -1.662615 -1.377490
5 -0.154816  0.617671 -0.090484 -0.191906
6 -0.705177 -1.086138 -0.629708  1.332853
7  0.637496 -0.643773 -0.492668 -0.777344
8  1.109497 -0.610165  0.260325  2.533383
9 -1.224584  0.117668  1.304369 -0.152561

In [11]: df['lat_long'] = df[['lat', 'long']].apply(tuple, axis=1)

In [12]: df
Out[12]:
          A         B       lat      long                             lat_long
0  1.428987  0.614405  0.484370 -0.628298      (0.484370195967, -0.6282975278)
1 -0.485747  0.275096  0.497116  1.047605      (0.497115615839, 1.04760475074)
2  0.822527  0.340689  2.120676 -2.436831      (2.12067574274, -2.43683074367)
3  0.384719 -0.042070  1.426703 -0.634355      (1.42670326172, -0.63435462504)
4 -0.937442  2.520756 -1.662615 -1.377490     (-1.66261469102, -1.37749004179)
5 -0.154816  0.617671 -0.090484 -0.191906  (-0.0904840623396, -0.191905582481)
6 -0.705177 -1.086138 -0.629708  1.332853     (-0.629707821728, 1.33285348929)
7  0.637496 -0.643773 -0.492668 -0.777344   (-0.492667604075, -0.777344111021)
8  1.109497 -0.610165  0.260325  2.533383        (0.26032456699, 2.5333825651)
9 -1.224584  0.117668  1.304369 -0.152561     (1.30436900612, -0.152560909725)