Find index location of first occurrence of a specific partial string in pandas dataframe

Question

I am working with AFRINIC whois data. I am trying find the location of the first occurrence of a substring. In is example(pandas dataframe with one column "data" to the left is the index) I am trying to find the substring, "inetnum". So the result i am looking for is "2750383".

    data
20  mnt-lower: info
21  changed: info
22  changed: info
23  changed: info
24  changed: info
25  source: info
26  as-block: info
27  type: info
28  descr: info
29  remarks: info
... ...
2750381 changed: info
2750382 source: info
2750383 inetnum: info
2750384 netname: info
2750385 descr: info
2750386 country: info
2750387 admin-c: info
2750388 tech-c: info
2750389 status: info

I have tried:

afrinic.data.ne('inetnum').idxmax()

But that gives me "[]" because it is looking for the first whole string and produces 0 results.

I have also tried:

indices = [i for i, s in enumerate(afrinic.data) if 'inetnum' in s]

But this gives me every index number of the word "inetnum" when all I am looking for is the first instance index number. I could take that list and just use the top number but there has to be a better way to do this. Any help is greatly appreciated.

Answer 1

Use str.contains for check in any position:

out = afrinic.data.str.contains('inetnum').idxmax()
print (out)
2750383

Or str.startswith for test start of each string:

out = afrinic.data.str.startswith('inetnum').idxmax()

EDIT:

More general solution for working if no value matched:

m = afrinic.data.str.contains('inetnum')
out = m.idxmax() if m.any() else 'no match'

Answer 2

You are very close. Use next with a generator expression:

first_index = next((i for i, s in enumerate(afrinic.data) if 'inetnum' in s), None)

If no valid index is found, then next will return None as per the fallback argument.

Using next with manual iteration will be more efficient than Pandas / NumPy-based methods for larger dataframes where you expect a match near the beginning of your series. Otherwise, you can use pd.Series.str methods as per @jezrael's solution.