Get last "column" after .str.split() operation on column in pandas DataFrame

Question

I have a column in a pandas DataFrame that I would like to split on a single space. The splitting is simple enough with DataFrame.str.split(' '), but I can't make a new column from the last entry. When I .str.split() the column I get a list of arrays and I don't know how to manipulate this to get a new column for my DataFrame.

Here is an example. Each entry in the column contains 'symbol data price' and I would like to split off the price (and eventually remove the "p"... or "c" in half the cases).

import pandas as pd temp = pd.DataFrame({'ticker' : ['spx 5/25/2001 p500', 'spx 5/25/2001 p600', 'spx 5/25/2001 p700']}) temp2 = temp.ticker.str.split(' ') 

which yields

0    ['spx', '5/25/2001', 'p500'] 1    ['spx', '5/25/2001', 'p600'] 2    ['spx', '5/25/2001', 'p700'] 

But temp2[0] just gives one list entry's array and temp2[:][-1] fails. How can I convert the last entry in each array to a new column? Thanks!

Answer 1

Do this:

In [43]: temp2.str[-1] Out[43]:  0    p500 1    p600 2    p700 Name: ticker 

So all together it would be:

>>> temp = pd.DataFrame({'ticker' : ['spx 5/25/2001 p500', 'spx 5/25/2001 p600', 'spx 5/25/2001 p700']}) >>> temp['ticker'].str.split(' ').str[-1] 0    p500 1    p600 2    p700 Name: ticker, dtype: object 

Answer 2

You could use the tolist method as an intermediary:

In [99]: import pandas as pd  In [100]: d1 = pd.DataFrame({'ticker' : ['spx 5/25/2001 p500', 'spx 5/25/2001 p600', 'spx 5/25/2001 p700']})  In [101]: d1.ticker.str.split().tolist() Out[101]:  [['spx', '5/25/2001', 'p500'],  ['spx', '5/25/2001', 'p600'],  ['spx', '5/25/2001', 'p700']] 

From which you could make a new DataFrame:

In [102]: d2 = pd.DataFrame(d1.ticker.str.split().tolist(),     .....:                   columns="symbol date price".split())  In [103]: d2 Out[103]:    symbol       date price 0    spx  5/25/2001  p500 1    spx  5/25/2001  p600 2    spx  5/25/2001  p700 

For good measure, you could fix the price:

In [104]: d2["price"] = d2["price"].str.replace("p","").astype(float)  In [105]: d2 Out[105]:    symbol       date  price 0    spx  5/25/2001    500 1    spx  5/25/2001    600 2    spx  5/25/2001    700 

PS: but if you really just want the last column, apply would suffice:

In [113]: temp2.apply(lambda x: x[2]) Out[113]:  0    p500 1    p600 2    p700 Name: ticker