How do you sort a dataframe with the integer in a column with strings and integers on every row?

Question

How would you sort the following dataframe:

df = pd.DataFrame({'a':['abc_1.2.6','abc_1.2.60','abc_1.2.7','abc_1.2.9','abc_1.3.0','abc_1.3.10','abc_1.3.100','abc_1.3.11'], 'b':[1,2,3,4,5,6,7,8]})

>>>
    a           b
0   abc_1.2.6   1
1   abc_1.2.60  2
2   abc_1.2.7   3
3   abc_1.2.9   4
4   abc_1.3.0   5
5   abc_1.3.10  6
6   abc_1.3.100 7
7   abc_1.3.11  8

to achieve this output?

>>>
    a           b
0   abc_1.2.6   1
1   abc_1.2.7   3
2   abc_1.2.9   4
3   abc_1.2.60  2
4   abc_1.3.0   5
5   abc_1.3.10  6
6   abc_1.3.11  8
7   abc_1.3.100 7

I understand that integers in strings can be accessed through string transformations, however I'm unsure how to handle this in a dataframe. Obviously df.sort_values(by=['a'],ignore_index=True) is unhelpful in this case.

Answer 1

One way to use is natsorted with iloc :

#pip install natsort
from natsort import natsorted
​
out = df.iloc[natsorted(range(len(df)), key=lambda x: df.loc[x, "a"])]

Or even shorter, as suggested by @Stef, use natsort_key as a key of sort_values :

from natsort import natsort_key

out = df.sort_values(by="a", key=natsort_key, ignore_index=True)

Output :

print(out)
             a  b
0    abc_1.2.6  1
1    abc_1.2.7  3
2    abc_1.2.9  4
3   abc_1.2.60  2
4    abc_1.3.0  5
5   abc_1.3.10  6
6   abc_1.3.11  8
7  abc_1.3.100  7