根据子字符串位置检索两个未知索引之间的列中的单元格字符串值

Question

I need to locate the first location where the word 'then' appears on Words table. I'm trying to get a code to consolidate all strings on 'text' column from this location till the first text with a substring '666' or '999' in it (in this case a combination of their, stoma22, fe156, sligh334, pain666 (the desired subtrings_output = 'theirfe156sligh334pain666'). I've tried:

their_loc = np.where(words['text'].str.contains(r'their', na =True))[0][0]
666_999_loc = np.where(words['text'].str.contains(r'666', na =True))[0][0]
subtrings_output = Words['text'].loc[Words.index[their_loc:666_999_loc]]

as you can see I'm not sure how to extend the conditioning of 666_999_loc to include substring 666 or 999, also slicing the indexing between two variables renders an error. Many thanks

Words table:

page no	text	font
1	they	0
1	ate	0
1	apples	0
2	and	0
2	then	1
2	their	0
2	stoma22	0
2	fe156	1
2	sligh334	0
2	pain666	1
2	given	0
2	the	1
3	fruit	0

Answer 1

You just need to add one for the end of the slice, and add an or condition to the np.where of the 666_or_999_loc using the | operator.

text_col = words['text']

their_loc = np.where(text_col.str.contains(r'their', na=True))[0][0]

contains_666_or_999_loc = np.where(text_col.str.contains('666', na=True) |
                                   text_col.str.contains('999', na=True))[0][0]

subtrings_output = ''.join(text_col.loc[words.index[their_loc:contains_666_or_999_loc + 1]])

print(subtrings_output)

---

Output:

theirstoma22fe156sligh334pain666

Answer 2

IIUC, use pandas.Series.idxmax with "".join() .

Series.idxmax(axis=0, skipna=True, *args, **kwargs)
Return the row label of the maximum value . If multiple values equal the maximum, the first row label with that value is returned.

So, assuming ( Words ) is your dataframe, try this:

their_loc = Words["text"].str.contains("their").idxmax()

_666_999_loc = Words["text"].str.contains("666").idxmax()

subtrings_output = "".join(Words["text"].loc[Words.index[their_loc:_666_999_loc+1]])

Output:

print(subtrings_output)
#theirstoma22fe156sligh334pain666

#their stoma22 fe156 sligh334 pain666 # <- with " ".join()