如何操作数据框，以便我访问单元格内列表中的每个元素并根据另一列对它们进行分组？

Question

this might be confusing so this is a copy of the first 5 rows of the data frame.

number  cap words
0   ['Ages', 'Online', 'Python', 'Coding', 'CoursesAdwwwcodetodaycoukLearn', 'Python', 'Live', 'Taught', 'Experts', 'Making', 'Coding', 'Fun', 'Courses', 'Summer', 'Weekly', 'EthosSimple', 'Low', 'Cost', 'PricingFAQAccess', 'Free', 'Content']
1   ['Become', 'Python', 'Programmer', 'Study', 'Python', 'Online', 'FreeAdwwwpythoninstituteorgLearn', 'Python', 'Become', 'Python', 'Certified', 'Take', 'Your', 'Career', 'Next', 'Level', 'Kostenfreie', 'Lernplattform', 'Tausende', 'Studenten', 'Lass', 'Dich', 'Highlights', 'Offering', 'SelfStudy', 'Courses', 'Free', 'Courses', 'Available', 'Flexible', 'DeadlinesResources', 'Free', 'Education', 'Platform', 'Get', 'Certification', 'About']
2   ['Python', 'For', 'Beginners', 'Pythonorgwwwpythonorg', 'Python', 'Its', 'NonProgrammers', 'Python', 'Programmers', 'Python', 'Frequently', 'Asked', 'Books']
3   ['People', 'Python', 'I', 'PythonIs', 'Python', 'Python']
4   ['PythonHighlevel', 'Created', 'Guido', 'Rossum', 'Pythons', 'WikipediaTyping', 'Duck', 'July', 'August', 'Guido', 'RossumOS', 'Linux', 'Windows', 'Vista', 'IDEsIDLEPyCharmMicrosoft', 'Visual', 'StudioSpyderEclipsePyDevPeople']
5   ['Welcome', 'PythonorgwwwpythonorgThe', 'Python', 'Programming', 'Language', 'Python', 'For', 'Beginners', 'Beginners', 'Guide', 'Python', 'Docs', 'Python', 'Books']
6   ['BeginnersGuide', 'Python', 'Wikiwikipythonorg', 'BeginnersGuide4', 'Jul', 'New', 'Python', 'This', 'Chinese']
7   ['Learn', 'Python', 'Codecademywwwcodecademycom', 'Python', 'By']
8   ['Python', 'Wikipediaenwikipediaorg', 'PythonprogramminglanguagePython', 'Created', 'Guido', 'Rossum', 'Pythons', 'History', 'Features', 'Syntax', 'Python', 'Developer', 'Python', 'Software', 'Foundation', 'Paradigm', 'Multiparadigm', 'Designed', 'Guido', 'Rossum', 'Typing', 'Duck']
9   ['Related']

I am trying to unpack every word from the list inside the cell and group them all under their shared index number.

so like that

    0
0   Ages
0   Online
0   Python
0   Coding
0   CoursesAdwwwcodetodaycoukLearn
0   Python
0   Live
0   Taught
0   Experts
0   Making
0   Coding
0   Fun
0   Courses
0   Summer
0   Weekly
0   EthosSimple
0   Low
0   Cost
0   PricingFAQAccess
0   Free
0   Content

and below follows 1 for the words 'Become', 'Python', 'Programmer', 'Study', 'Python', 'Online' etc...

I hope this is clear.

Thanks

Answer 1

You can use explode

x = [['Ages', 'Online', 'Python', 'Coding', 'CoursesAdwwwcodetodaycoukLearn', 'Python', 'Live', 'Taught', 'Experts', 'Making', 'Coding', 'Fun', 'Courses', 'Summer', 'Weekly', 'EthosSimple', 'Low', 'Cost', 'PricingFAQAccess', 'Free', 'Content']
,['Become', 'Python', 'Programmer', 'Study', 'Python', 'Online', 'FreeAdwwwpythoninstituteorgLearn', 'Python', 'Become', 'Python', 'Certified', 'Take', 'Your', 'Career', 'Next', 'Level', 'Kostenfreie', 'Lernplattform', 'Tausende', 'Studenten', 'Lass', 'Dich', 'Highlights', 'Offering', 'SelfStudy', 'Courses', 'Free', 'Courses', 'Available', 'Flexible', 'DeadlinesResources', 'Free', 'Education', 'Platform', 'Get', 'Certification', 'About']
,['Python', 'For', 'Beginners', 'Pythonorgwwwpythonorg', 'Python', 'Its', 'NonProgrammers', 'Python', 'Programmers', 'Python', 'Frequently', 'Asked', 'Books']
,['People', 'Python', 'I', 'PythonIs', 'Python', 'Python']
,['PythonHighlevel', 'Created', 'Guido', 'Rossum', 'Pythons', 'WikipediaTyping', 'Duck', 'July', 'August', 'Guido', 'RossumOS', 'Linux', 'Windows', 'Vista', 'IDEsIDLEPyCharmMicrosoft', 'Visual', 'StudioSpyderEclipsePyDevPeople']
,['Welcome', 'PythonorgwwwpythonorgThe', 'Python', 'Programming', 'Language', 'Python', 'For', 'Beginners', 'Beginners', 'Guide', 'Python', 'Docs', 'Python', 'Books']
,['BeginnersGuide', 'Python', 'Wikiwikipythonorg', 'BeginnersGuide4', 'Jul', 'New', 'Python', 'This', 'Chinese']
,['Learn', 'Python', 'Codecademywwwcodecademycom', 'Python', 'By']
,['Python', 'Wikipediaenwikipediaorg', 'PythonprogramminglanguagePython', 'Created', 'Guido', 'Rossum', 'Pythons', 'History', 'Features', 'Syntax', 'Python', 'Developer', 'Python', 'Software', 'Foundation', 'Paradigm', 'Multiparadigm', 'Designed', 'Guido', 'Rossum', 'Typing', 'Duck']
,['Related']]

df = pd.DataFrame({
    'number': np.arange(10),
    'cap words' :pd.Series(x)
})

df.explode('cap words').reset_index(drop=True)

Out:

     number                       cap words
0         0                            Ages
1         0                          Online
2         0                          Python
3         0                          Coding
4         0  CoursesAdwwwcodetodaycoukLearn
..      ...                             ...
140       8                           Guido
141       8                          Rossum
142       8                          Typing
143       8                            Duck
144       9                         Related

[145 rows x 2 columns]