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Concatenate list element of three different based on index into one list

I have the following three lists of which I'd like to combine each list element by index creating a new list.
Before:
list_numbers = [55900, 44560, 49510, 49509, 49519, 49556, 49586]
list_names = ['Richard White', 'Susan Pierce', 'Kim Note', 'John Lee', 'Jennifer Six', 'Maria Cruz', 'Martin Lewis']
list_grades = ['100', '46', '76', '74', '50', '67', '79']
What I'd like to receive as a result:
list_final = ['55900 Richard White 100', '44560 Susan Pierce 46', '49510 Kim Note 76', '49509 John Lee 74', '49519 Jennifer Six 50', '49556 Maria Cruz 67', '49586 Martin Lewis 79']
Once the list is created it is supposed to be sorted by the first 4 numbers in the character list elements.
Thanks, everyone!

You can try to run loop and concatenate all values as single string as below:
[f'{list_numbers[l]} {list_names[l]} {list_grades[l]}' for l in range(len(list_grades))]
Or
[' '.join(map(str, z)) for z in zip(list_numbers,list_names, list_grades )]
Output:
['55900 Richard White 100',
'44560 Susan Pierce 46',
'49510 Kim Note 76',
'49509 John Lee 74',
'49519 Jennifer Six 50',
'49556 Maria Cruz 67',
'49586 Martin Lewis 79']

new= [f"{a} {b} {c}" for a,b,c in zip(list_numbers,list_names, list_grades )]

cleaning up web scrape data and combining together?

The website URL is https://www.justia.com/lawyers/criminal-law/maine
I'm wanting to scrape only the name of the lawyer and where their office is.
response = requests.get(url)
soup= BeautifulSoup(response.text,"html.parser")
Lawyer_name= soup.find_all("a","url main-profile-link")
for i in Lawyer_name:
print(i.find(text=True))
address= soup.find_all("span","-address -hide-landscape-tablet")
for x in address:
print(x.find_all(text=True))
The name prints out just find but the address is printing off with extra that I want to remove:
['\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t88 Hammond Street', '\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tBangor,\t\t\t\t\tME 04401\t\t\t\t\t\t ']
so the output I'm attempting to get for each lawyer is like this (the 1st one example):
Hunter J Tzovarras
88 Hammond Street
Bangor, ME 04401
two issues I'm trying to figure out
How can I clean up the address so it is easier to read?
How can I save the matching lawyer name with the address so they
don't get mixed up.

Use x.get_text() instead of x.find_all
for x in address:
print(x.get_text(strip=True))
Full working code:
import pandas as pd
import requests
from bs4 import BeautifulSoup
url = 'https://www.justia.com/lawyers/criminal-law/maine'
response = requests.get(url)
soup= BeautifulSoup(response.text,"html.parser")
n=[]
ad=[]
Lawyer_name= [x.get('title').strip() for x in soup.select('a.lawyer-avatar')]
n.extend(Lawyer_name)
#print(Lawyer_name)
address= [x.get_text(strip=True).replace('\t','').strip() for x in soup.find_all("span",class_="-address -hide-landscape-tablet")]
#print(address)
ad.extend(address)
df = pd.DataFrame(data=list(zip(n,ad)),columns=[['Lawyer_name','address']])
print(df)
Output:
Lawyer_name address
0 William T. Bly Esq 119 Main StreetKennebunk,ME 04043
1 John S. Webb 949 Main StreetSanford,ME 04073
2 William T. Bly Esq 20 Oak StreetEllsworth,ME 04605
3 Christopher Causey Esq 16 Middle StSaco,ME 04072
4 Robert Van Horn 88 Hammond StreetBangor,ME 04401
5 John S. Webb 37 Western Ave., Unit #307Kennebunk,ME 04043
6 Hunter J Tzovarras 4 Union Park RoadTopsham,ME 04086
7 Michael Stephen Bowser Jr. 241 Main StreetP.O. Box 57Saco,ME 04072
8 Richard Regan 6 City CenterSuite 301Portland,ME 04101
9 Robert Guillory Esq 75 Pearl St. Suite 400Portland,ME 04101
10 Dylan R. Boyd 160 Capitol StreetP.O. Box 79Augusta,ME 04332
11 Luke Rioux Esq 10 Stoney Brook LaneLyman,ME 04002
12 David G. Webbert 15 Columbia Street, Ste. 301Bangor,ME 04401
13 Amy Fairfield 32 Saco AveOld Orchard Beach,ME 04064
14 Mr. Richard Lyman Hartley 62 Portland Rd., Ste. 44Kennebunk,ME 04043
15 Neal L Weinstein Esq 647 U.S. Route One#203York,ME 03909
16 Albert Hansen 76 Tandberg Trail (Route 115)Windham,ME 04062
17 Russell Goldsmith Esq Two Canal PlazaPO Box 4600Portland,ME 04112
18 Miklos Pongratz Esq 18 Market Square Suite 5Houlton,ME 04730
19 Bradford Pattershall Esq 5 Island View DrCumberland Foreside,ME 04110
20 Michele D L Kenney 12 Silver StreetP.O. Box 559Waterville,ME 04903
21 John Simpson 344 Mount Hope Ave.Bangor,ME 04402
22 Mariah America Gleaton 192 Main StreetEllsworth,ME 04605
23 Wayne Foote Esq 85 Brackett StreetPortland,ME 04102
24 Will Ashe 16 Union StreetBrunswick,ME 04011
25 Peter J Cyr Esq 482 Congress Street Suite 402Portland,ME 04101
26 Jonathan Steven Handelman Esq PO Box 335York,ME 03909
27 Richard Smith Berne 36 Ossipee Trl W.Standish,ME 04084
28 Meredith G. Schmid 75 Pearl St.Suite 216Portland,ME 04101
29 Gregory LeClerc 28 Long Sands Road, Suite 5York,ME 03909
30 Cory McKenna 20 Mechanic StCamden,ME 04843
31 Thomas P. Elias P.O. Box 1049304 Hancock St. Suite 1KBangor,ME...
32 Christopher MacLean 1250 Forest Avenue, Ste 3APortland,ME 04103
33 Zachary J. Smith 415 Congress StreetSuite 202Portland,ME 04101
34 Stephen Sweatt 919 Ridge RoadP.O. BOX 119Bowdoinham,ME 04008
35 Michael Turndorf Esq 1250 Forest Avenue, Ste 3APortland,ME 04103
36 Andrews Bruce Campbell Esq 133 State StreetAugusta,ME 04330
37 Timothy Zerillo 110 Portland StreetFryeburg,ME 04037
38 Walter McKee Esq 440 Walnut Hill RdNorth Yarmouth,ME 04097
39 Shelley Carter 70 State StreetEllsworth,ME 04605

for your second query You can save them into a dictionary like this -
url = 'https://www.justia.com/lawyers/criminal-law/maine'
response = requests.get(url)
soup= BeautifulSoup(response.text,"html.parser")
# parse all names and save them in a list
lawyer_names = soup.find_all("a","url main-profile-link")
lawyer_names = [name.find(text=True).strip() for name in lawyer_names]
# parse all addresses and save them in a list
lawyer_addresses = soup.find_all("span","-address -hide-landscape-tablet")
lawyer_addresses = [re.sub('\s+',' ', address.get_text(strip=True)) for address in lawyer_addresses]
# map names with addresses
lawyer_dict = dict(zip(lawyer_names, lawyer_addresses))
print(lawyer_dict)
Output dictionary -
{'Albert Hansen': '62 Portland Rd., Ste. 44Kennebunk, ME 04043',
'Amber Lynn Tucker': '415 Congress St., Ste. 202P.O. Box 7542Portland, ME 04112',
'Amy Fairfield': '10 Stoney Brook LaneLyman, ME 04002',
'Andrews Bruce Campbell Esq': '919 Ridge RoadP.O. BOX 119Bowdoinham, ME 04008',
'Bradford Pattershall Esq': 'Two Canal PlazaPO Box 4600Portland, ME 04112',
'Christopher Causey Esq': '949 Main StreetSanford, ME 04073',
'Cory McKenna': '75 Pearl St.Suite 216Portland, ME 04101',
'David G. Webbert': '160 Capitol StreetP.O. Box 79Augusta, ME 04332',
'David Nelson Wood Esq': '120 Main StreetSuite 110Saco, ME 04072',
'Dylan R. Boyd': '6 City CenterSuite 301Portland, ME 04101',
'Gregory LeClerc': '36 Ossipee Trl W.Standish, ME 04084',
'Hunter J Tzovarras': '88 Hammond StreetBangor, ME 04401',
'John S. Webb': '16 Middle StSaco, ME 04072',
'John Simpson': '5 Island View DrCumberland Foreside, ME 04110',
'Jonathan Steven Handelman Esq': '16 Union StreetBrunswick, ME 04011',
'Luke Rioux Esq': '75 Pearl St. Suite 400Portland, ME 04101',
'Mariah America Gleaton': '12 Silver StreetP.O. Box 559Waterville, ME 04903',
'Meredith G. Schmid': 'PO Box 335York, ME 03909',
'Michael Stephen Bowser Jr.': '37 Western Ave., Unit #307Kennebunk, ME 04043',
'Michael Turndorf Esq': '415 Congress StreetSuite 202Portland, ME 04101',
'Michele D L Kenney': '18 Market Square Suite 5Houlton, ME 04730',
'Miklos Pongratz Esq': '76 Tandberg Trail (Route 115)Windham, ME 04062',
'Mr. Richard Lyman Hartley': '15 Columbia Street, Ste. 301Bangor, ME 04401',
'Neal L Weinstein Esq': '32 Saco AveOld Orchard Beach, ME 04064',
'Peter J Cyr Esq': '85 Brackett StreetPortland, ME 04102',
'Richard Regan': '4 Union Park RoadTopsham, ME 04086',
'Richard Smith Berne': '482 Congress Street Suite 402Portland, ME 04101',
'Robert Guillory Esq': '241 Main StreetP.O. Box 57Saco, ME 04072',
'Robert Van Horn': '20 Oak StreetEllsworth, ME 04605',
'Russell Goldsmith Esq': '647 U.S. Route One#203York, ME 03909',
'Shelley Carter': '110 Portland StreetFryeburg, ME 04037',
'Thaddeus Day Esq': '440 Walnut Hill RdNorth Yarmouth, ME 04097',
'Thomas P. Elias': '28 Long Sands Road, Suite 5York, ME 03909',
'Timothy Zerillo': '1250 Forest Avenue, Ste 3APortland, ME 04103',
'Todd H Crawford Jr': '1288 Roosevelt Trl, Ste #3P.O. Box 753Raymond, ME 04071',
'Walter McKee Esq': '133 State StreetAugusta, ME 04330',
'Wayne Foote Esq': '344 Mount Hope Ave.Bangor, ME 04402',
'Will Ashe': '192 Main StreetEllsworth, ME 04605',
'William T. Bly Esq': '119 Main StreetKennebunk, ME 04043',
'Zachary J. Smith': 'P.O. Box 1049304 Hancock St. Suite 1KBangor, ME 04401'}

webscraping stars from imdb page using beautifulsoup

I am trying to get the name of stars from an IMDb page. below is my code
from requests import get
url = 'https://www.imdb.com/search/title/?title_type=tv_movie,tv_series&user_rating=6.0,10.0&adult=include&ref_=adv_prv'
response = get(url)
from bs4 import BeautifulSoup
html_soup = BeautifulSoup(response.text, 'html.parser')
movie_containers = html_soup.find_all('div', 'lister-item mode-advanced')
first_movie = movie_containers[0]
first_stars = first_movie.select('a[href*="name"]')
first_stars
I got the following output
[Bob Odenkirk,
Rhea Seehorn,
Jonathan Banks,
Michael Mando]
i am trying to get only the names of the stars and first_stars.text gives the following error
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_3104\1297903165.py in <module>
1 first_stars = first_movie.select('a[href*="name"]')
----> 2 first_stars.text
~\Anaconda3\lib\site-packages\bs4\element.py in __getattr__(self, key)
2288 """Raise a helpful exception to explain a common code fix."""
2289 raise AttributeError(
-> 2290 "ResultSet object has no attribute '%s'. You're probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?" % key
2291 )
AttributeError: ResultSet object has no attribute 'text'. You're probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?
when i tried
first_stars = first_movie.find('a[href*="name"]')
first_stars.text
i also got the following error
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_3104\2359725208.py in <module>
1 first_stars = first_movie.find('a[href*="name"]')
----> 2 first_stars.text
AttributeError: 'NoneType' object has no attribute 'text'
Any idea how i can extract only the name of the stars?

If you need the star name without distinction this might help.
block = soup.find_all("div", attrs={"class":"lister-item mode-advanced"})
starList= list()
for star in block:
starList.append(star.find("p", attrs={"class":""}).text.replace("Stars:", "").replace("\n", "").strip())
print(starList)
It prints
['Bob Odenkirk, Rhea Seehorn, Jonathan Banks, Michael Mando', 'Jason Bateman, Laura Linney, Sofia Hublitz, Skylar Gaertner', 'Gia Sandhu, Anson Mount, Ethan Peck, Jess Bush', 'Josh Brolin, Imogen Poots, Lili Taylor, Tom Pelphrey', 'Pablo Schreiber, Shabana Azmi, Natasha Culzac, Olive Gray', 'Titus Welliver, Mimi Rogers, Madison Lintz, Stephen A. Chang', 'Rachel Griffiths, Sophia Ali, Shannon Berry, Jenna Clause', 'Adam Scott, Zach Cherry, Britt Lower, Tramell Tillman', 'Milo Ventimiglia, Mandy Moore, Sterling K. Brown, Chrissy Metz', 'Emilia Clarke, Peter Dinklage, Kit Harington, Lena Headey', 'Bryan Cranston, Aaron Paul, Anna Gunn, Betsy Brandt', 'Millie Bobby Brown, Finn Wolfhard, Winona Ryder, David Harbour', 'Joe Locke, Kit Connor, Yasmin Finney, William Gao', 'John C. Reilly, Quincy Isaiah, Jason Clarke, Gaby Hoffmann', 'Bill Hader, Stephen Root, Sarah Goldberg, Anthony Carrigan', 'Kaley Cuoco, Zosia Mamet, Griffin Matthews, Rosie Perez', 'Caitríona Balfe, Sam Heughan, Sophie Skelton, Richard Rankin', 'Evan Rachel Wood, Jeffrey Wright, Ed Harris, Thandiwe Newton', 'Patrick Stewart, Alison Pill, Michelle Hurd, Santiago Cabrera', 'Nicola Coughlan, Jonathan Bailey, Ruth Gemmell, Florence Hunt', 'Andrew Lincoln, Norman Reedus, Melissa McBride, Lauren Cohan', 'Cillian Murphy, Paul Anderson, Sophie Rundle, Helen McCrory', 'Manuel Garcia-Rulfo, Becki Newton, Neve Campbell, Christopher Gorham', 'Jane Fonda, Lily Tomlin, Sam Waterston, Martin Sheen', 'Ellen Pompeo, Chandra Wilson, James Pickens Jr., Justin Chambers', 'Alexander Dreymon, Eliza Butterworth, Arnas Fedaravicius, Mark Rowley', 'Luke Grimes, Kelly Reilly, Wes Bentley, Cole Hauser', 'Elisabeth Moss, Wagner Moura, Phillipa Soo, Chris Chalk', "Scott Whyte, Nolan North, Steven Pacey, Emily O'Brien", 'Steve Carell, Jenna Fischer, John Krasinski, Rainn Wilson', 'Jodie Whittaker, Peter Capaldi, Pearl Mackie, Matt Smith', 'Ansel
Elgort, Ken Watanabe, Rachel Keller, Shô Kasamatsu', 'James Spader, Megan Boone, Diego Klattenhoff, Ryan Eggold', 'Mark Harmon, David McCallum, Sean Murray, Pauley Perrette', 'Zendaya, Hunter Schafer, Angus Cloud, Jacob Elordi', 'Niv Sultan, Shaun Toub, Shervin Alenabi, Arash Marandi', 'Asa Butterfield, Gillian Anderson, Emma Mackey, Ncuti Gatwa', 'Jack Lowden, Kristin Scott Thomas, Gary Oldman, Chris Reilly', 'Karl Urban, Jack Quaid, Antony Starr, Erin Moriarty', 'Mariska Hargitay, Christopher Meloni, Ice-T, Dann Florek', "Nathan Fillion, Alyssa Diaz, Richard T. Jones, Melissa O'Neil", "Saoirse-Monica Jackson, Louisa Harland, Tara Lynne O'Neill, Kathy Kiera Clarke", 'Donald Glover, Brian Tyree Henry, LaKeith Stanfield, Zazie Beetz', 'Jennifer Aniston, Courteney Cox, Lisa Kudrow, Matt LeBlanc', 'Jared Padalecki, Jensen Ackles, Jim Beaver, Misha Collins', 'Julia Roberts, Sean Penn, Dan Stevens, Betty Gilpin', 'James Gandolfini, Lorraine Bracco, Edie Falco, Michael Imperioli', 'Natasha Lyonne, Charlie Barnett, Greta Lee, Elizabeth Ashley', 'Jean Smart, Hannah Einbinder, Carl Clemons-Hopkins, Rose Abdoo', 'Katheryn Winnick, Gustaf Skarsgård, Alexander Ludwig, Georgia Hirst']
or if you need both title and its stars
block = soup.find_all("div", attrs={"class":"lister-item mode-advanced"})
starList= list()
movieDict = dict()
for star in block:
movieDict = {
"moviename":star.find("h3", attrs={"class":"lister-item-header"}).text.split("\n")[2],
"stars": star.find("p", attrs={"class":""}).text.replace("Stars:", "").replace("\n", "").strip()
}
starList.append(movieDict)
print(starList)
this will print
[{'moviename': 'Better Call Saul', 'stars': 'Bob Odenkirk, Rhea Seehorn, Jonathan Banks, Michael
Mando'}, {'moviename': 'Ozark', 'stars': 'Jason Bateman, Laura Linney, Sofia Hublitz, Skylar Gaertner'}, {'moviename': 'Star Trek: Strange New Worlds', 'stars': 'Gia Sandhu, Anson Mount, Ethan Peck, Jess Bush'}, {'moviename': 'Outer Range', 'stars': 'Josh Brolin, Imogen Poots, Lili Taylor,
Tom Pelphrey'}, {'moviename': 'Halo', 'stars': 'Pablo Schreiber, Shabana Azmi, Natasha Culzac, Olive Gray'}, {'moviename': 'Bosch: Legacy', 'stars': 'Titus Welliver, Mimi Rogers, Madison Lintz,
Stephen A. Chang'}, {'moviename': 'The Wilds', 'stars': 'Rachel Griffiths, Sophia Ali, Shannon Berry, Jenna Clause'}, {'moviename': 'Severance', 'stars': 'Adam Scott, Zach Cherry, Britt Lower, Tramell Tillman'}, {'moviename': 'This Is Us', 'stars': 'Milo Ventimiglia, Mandy Moore, Sterling K. Brown, Chrissy Metz'}, {'moviename': 'Game of Thrones', 'stars': 'Emilia Clarke, Peter Dinklage, Kit Harington, Lena Headey'}, {'moviename': 'Breaking Bad', 'stars': 'Bryan Cranston, Aaron Paul, Anna Gunn, Betsy Brandt'}, {'moviename': 'Stranger Things', 'stars': 'Millie Bobby Brown, Finn Wolfhard, Winona Ryder, David Harbour'}, {'moviename': 'Heartstopper', 'stars': 'Joe Locke, Kit
Connor, Yasmin Finney, William Gao'}, {'moviename': 'Winning Time: The Rise of the Lakers Dynasty', 'stars': 'John C. Reilly, Quincy Isaiah, Jason Clarke, Gaby Hoffmann'}, {'moviename': 'Barry', 'stars': 'Bill Hader, Stephen Root, Sarah Goldberg, Anthony Carrigan'}, {'moviename': 'The Flight Attendant', 'stars': 'Kaley Cuoco, Zosia Mamet, Griffin Matthews, Rosie Perez'}, {'moviename':
'Outlander', 'stars': 'Caitríona Balfe, Sam Heughan, Sophie Skelton, Richard Rankin'}, {'moviename': 'Westworld', 'stars': 'Evan Rachel Wood, Jeffrey Wright, Ed Harris, Thandiwe Newton'}, {'moviename': 'Star Trek: Picard', 'stars': 'Patrick Stewart, Alison Pill, Michelle Hurd, Santiago Cabrera'}, {'moviename': 'Bridgerton', 'stars': 'Nicola Coughlan, Jonathan Bailey, Ruth Gemmell, Florence Hunt'}, {'moviename': 'The Walking Dead', 'stars': 'Andrew Lincoln, Norman Reedus, Melissa McBride, Lauren Cohan'}, {'moviename': 'Peaky Blinders', 'stars': 'Cillian Murphy, Paul Anderson,
Sophie Rundle, Helen McCrory'}, {'moviename': 'The Lincoln Lawyer', 'stars': 'Manuel Garcia-Rulfo, Becki Newton, Neve Campbell, Christopher Gorham'}, {'moviename': 'Grace and Frankie', 'stars':
'Jane Fonda, Lily Tomlin, Sam Waterston, Martin Sheen'}, {'moviename': "Grey's Anatomy", 'stars': 'Ellen Pompeo, Chandra Wilson, James Pickens Jr., Justin Chambers'}, {'moviename': 'The Last Kingdom', 'stars': 'Alexander Dreymon, Eliza Butterworth, Arnas Fedaravicius, Mark Rowley'}, {'moviename': 'Yellowstone', 'stars': 'Luke Grimes, Kelly Reilly, Wes Bentley, Cole Hauser'}, {'moviename': 'Shining Girls', 'stars': 'Elisabeth Moss, Wagner Moura, Phillipa Soo, Chris Chalk'}, {'moviename': 'Love, Death & Robots', 'stars': "Scott Whyte, Nolan North, Steven Pacey, Emily O'Brien"}, {'moviename': 'The Office', 'stars': 'Steve Carell, Jenna Fischer, John Krasinski, Rainn Wilson'}, {'moviename': 'Doctor Who', 'stars': 'Jodie Whittaker, Peter Capaldi, Pearl Mackie, Matt Smith'}, {'moviename': 'Tokyo Vice', 'stars': 'Ansel Elgort, Ken Watanabe, Rachel Keller, Shô Kasamatsu'}, {'moviename': 'The Blacklist', 'stars': 'James Spader, Megan Boone, Diego Klattenhoff, Ryan
Eggold'}, {'moviename': 'NCIS: Naval Criminal Investigative Service', 'stars': 'Mark Harmon, David McCallum, Sean Murray, Pauley Perrette'}, {'moviename': 'Euphoria', 'stars': 'Zendaya, Hunter Schafer, Angus Cloud, Jacob Elordi'}, {'moviename': 'Tehran', 'stars': 'Niv Sultan, Shaun Toub, Shervin Alenabi, Arash Marandi'}, {'moviename': 'Sex Education', 'stars': 'Asa Butterfield, Gillian Anderson, Emma Mackey, Ncuti Gatwa'}, {'moviename': 'Slow Horses', 'stars': 'Jack Lowden, Kristin Scott Thomas, Gary Oldman, Chris Reilly'}, {'moviename': 'The Boys', 'stars': 'Karl Urban, Jack Quaid, Antony Starr, Erin Moriarty'}, {'moviename': 'Law & Order: Special Victims Unit', 'stars': 'Mariska Hargitay, Christopher Meloni, Ice-T, Dann Florek'}, {'moviename': 'The Rookie', 'stars': "Nathan Fillion, Alyssa Diaz, Richard T. Jones, Melissa O'Neil"}, {'moviename': 'Derry Girls', 'stars': "Saoirse-Monica Jackson, Louisa Harland, Tara Lynne O'Neill, Kathy Kiera Clarke"}, {'moviename': 'Atlanta', 'stars': 'Donald Glover, Brian Tyree Henry, LaKeith Stanfield, Zazie Beetz'}, {'moviename': 'Friends', 'stars': 'Jennifer Aniston, Courteney Cox, Lisa Kudrow, Matt LeBlanc'}, {'moviename': 'Supernatural', 'stars': 'Jared Padalecki, Jensen Ackles, Jim Beaver, Misha Collins'}, {'moviename': 'Gaslit', 'stars': 'Julia Roberts, Sean Penn, Dan Stevens, Betty Gilpin'}, {'moviename': 'The Sopranos', 'stars': 'James Gandolfini, Lorraine Bracco, Edie Falco, Michael Imperioli'}, {'moviename': 'Russian Doll', 'stars': 'Natasha Lyonne, Charlie Barnett, Greta Lee, Elizabeth Ashley'}, {'moviename': 'Hacks', 'stars': 'Jean Smart, Hannah Einbinder, Carl Clemons-Hopkins, Rose Abdoo'}, {'moviename': 'Vikings', 'stars': 'Katheryn Winnick, Gustaf Skarsgård, Alexander Ludwig, Georgia Hirst'}]

You have to iterate the ResultSet:
first_stars = [s.text for s in first_movie.select('a[href*="name"]')]
first_stars
Output:
['Bob Odenkirk', 'Rhea Seehorn', 'Jonathan Banks', 'Michael Mando']

Delete the rows with repeated characters in the dataframe

I have a large dataset from csv file to clean with the patterns I've identified but I can't upload the file here so I've just hardcoded a small sample to give an overview of what I'm looking for. The identified patterns are the repeated characters in the values. However, if you look at the dataframe below, there are actually repeated 'single characters' like ssssss, fffff, aaaaa, etc and then the repeated 'double characters' like dgdg, bvbvbv, tutu, etc. There are also repeated 'triple characters' such as yutyut and fdgfdg.
Despite of this, would it be also possible to delete the rows with ANY repeated 'single/double/triple characters' so that I can apply them to the large dataset? For example, the dataframe here only shows the patterns I identified above, however, there could be repeated characters of ANY letters like 'uuuu', 'zzzz', 'eded, 'rsrsrs', 'xyzxyz', etc in the large dataset.
Address1 Address2 Address3 Address4
0 High Street Park Avenue St. John’s Road The Grove
1 wssssss The Crescent tyutyut Mill Road
2 qfdgfdgdg dddfffff qdffgfdgfggfbvbvbv sefsdfdyuytutu
3 Green Lane Highfield Road Springfield Road School Lane
4 Kingsway Stanley Road George Street Albert Road
5 Church Street New Street Queensway Broadway
6 qaaaaass mjkhjk chfghfghh fghfhfh
Here's the code:
import pandas as pd
import numpy as np
data = {'Address1': ['High Street', 'wssssss', 'qfdgfdgdg', 'Green Lane', 'Kingsway', 'Church Street', 'qaaaaass'],
'Address2': ['Park Avenue', 'The Crescent', 'dddfffff', 'Highfield Road', 'Stanley Road', 'New Street', 'mjkhjk'],
'Address3': ['St. John’s Road', 'tyutyut', 'qdffgfdgfggfbvbvbv', 'Springfield Road', 'George Street', 'Queensway', 'chfghfghh'],
'Address4': ['The Grove', 'Mill Road', 'sefsdfdyuytutu', 'School Lane', 'Albert Road', 'Broadway', 'fghfhfh']}
address_details = pd.DataFrame(data)
#Code to delete the data for the identified patterns
print(address_details)
The output I expect is:
Address1 Address2 Address3 Address4
0 High Street Park Avenue St. John’s Road The Grove
1 Green Lane Highfield Road Springfield Road School Lane
2 Kingsway Stanley Road George Street Albert Road
3 Church Street New Street Queensway Broadway
Please advise, thank you!

Try with str.contains and loc with agg:
print(address_details.loc[~address_details.agg(lambda x: x.str.contains(r"(.)\1+\b"), axis=1).any(1)])
Output:
Address1 Address2 Address3 Address4
0 High Street Park Avenue St. John’s Road The Grove
3 Green Lane Highfield Road Springfield Road School Lane
4 Kingsway Stanley Road George Street Albert Road
5 Church Street New Street Queensway Broadway
Or if you care about index:
print(address_details.loc[~address_details.agg(lambda x: x.str.contains(r"(.)\1+\b"), axis=1).any(1)].reset_index(drop=True))
Output:
Address1 Address2 Address3 Address4
0 High Street Park Avenue St. John’s Road The Grove
1 Green Lane Highfield Road Springfield Road School Lane
2 Kingsway Stanley Road George Street Albert Road
3 Church Street New Street Queensway Broadway
Edit:
For only lowercase letters, try:
print(address_details.loc[~address_details.agg(lambda x: x.str.contains(r"([a-z]+)\1{1,}\b"), axis=1).any(1)].reset_index(drop=True))

How to lookup multiple entries in table to calculate proportion?

I have a table of lots of sports teams. For each team I need to know the number of fans attending their game as a percentage of all the fans in that region with the same name suffix. The table below gives you an idea of what I'm working with:
Region Team Suffix Attending Fans
North West blue city city 181
North East Black and white united united 130
North West blue and white city city 101
North East Purple United united 12
North East red city city 73
North East red and white 112
North West Red city city 162
North East white shorts united united 93
North East orange and black city city 68
North West pink united united 4
North West red united united 192
North West orange united united 42
In the above example, the percentage of attending Red City fans as a proportion of the attending fans from all North West teams suffixed with 'city' is 36.48 %.
What I would like to know is
How to lookup the relevant elements so that I can perform the calculation?
How to automate this so it occurs for every team (including those that do not have a suffix)?

Here is one way. The idea is to perform a groupby.sum() and map this onto the dataframe as part of your calculation.
import pandas as pd, numpy as np
df = pd.DataFrame([['North West', 'blue city', 'city', 181],
['North East', 'Black and white united', 'united', 130],
['North West', 'blue and white city', 'city', 101],
['North East', 'Purple United', 'united', 12],
['North East', 'red city', 'city', 73],
['North East', 'red and white', '', 112],
['North West', 'Red city', 'city', 162],
['North East', 'white shorts united', 'united', 93],
['North East', 'orange and black city', 'city', 68],
['North West', 'pink united', 'united', 4],
['North West', 'red united', 'united', 192],
['North West', 'orange united', 'united', 42]],
columns=['Region', 'Team', 'Suffix', 'Attending Fans'])
g = df.groupby(['Region', 'Suffix'])['Attending Fans'].sum()
df['Pct'] = 100 * df['Attending Fans'] / np.fromiter(map(g.get,
map(tuple, df[['Region', 'Suffix']].values)), dtype=float)
# Region Team Suffix Attending Fans Pct
# 0 North West blue city city 181 40.765766
# 1 North East Black and white united united 130 55.319149
# 2 North West blue and white city city 101 22.747748
# 3 North East Purple United united 12 5.106383
# 4 North East red city city 73 51.773050
# 5 North East red and white 112 100.000000
# 6 North West Red city city 162 36.486486
# 7 North East white shorts united united 93 39.574468
# 8 North East orange and black city city 68 48.226950
# 9 North West pink united united 4 1.680672
# 10 North West red united united 192 80.672269
# 11 North West orange united united 42 17.647059