I have 2 pandas series that look like this:
import pandas as pd
listA = [5,4,3]
listB = ["a","b","c"]
s = pd.Series(listA)
print(s)
p = pd.Series(listB)
print(p)
And I would like to obtain a list of the 2 lists mixed together as strings like this:
listTogether = ["a5","a4","a3","b5","b4","b3","c5","c4","c3"]
t = pd.Series(listTogether)
print(t)
Do you have any hint? Is it possible to do by avoiding loops?
Thanks so much in advance for the help
A trick from MultiIndex
listTogether = pd.MultiIndex.from_product([p,s.astype(str)]).map(''.join).tolist()
listTogether
Out[242]: ['a5', 'a4', 'a3', 'b5', 'b4', 'b3', 'c5', 'c4', 'c3']
You are looping whether you like it or not.
[f'{b}{a}' for b in listB for a in listA]
['a5', 'a4', 'a3', 'b5', 'b4', 'b3', 'c5', 'c4', 'c3']
You can use itertools product
from itertools import product
pd.DataFrame(list(product(p.tolist(),s.astype(str).tolist()))).apply(''.join, axis = 1).tolist()
839 µs ± 18.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
['a5', 'a4', 'a3', 'b5', 'b4', 'b3', 'c5', 'c4', 'c3']
If you want a very efficient solution, go pure python way
[''.join(i) for i in list(product(p.tolist(),s.astype(str).tolist()))]
79 µs ± 924 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
The use of zip may help you.
You can do something like that for your lists, however it involves a for loop :
listTogether = ['{}{}'.format(a,b) for (a,b) in zip(listA,listB)]
zipand a comprehension/map.