How can I split columns with regex to move trailing CAPS into a separate column?

Question

I'm trying to split a column using regex, but can't seem to get the split correctly. I'm trying to take all the trailing CAPS and move them into a separate column. So I'm getting all the CAPS that are either 2-4 CAPS in a row. However, it's only leaving the 'Name' column while the 'Team' column is blank.

Here's my code:

import pandas as pd

url = "https://www.espn.com/nba/stats/player/_/table/offensive/sort/avgAssists/dir/desc"

df = pd.read_html(url)[0].join(pd.read_html(url)[1])
df[['Name','Team']] = df['Name'].str.split('[A-Z]{2,4}', expand=True)  

I want this:

print(df.head(5).to_string())
   RK             Name POS  GP   MIN   PTS  FGM   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  REB   AST  STL  BLK   TO  DD2  TD3    PER
0   1  LeBron JamesLA  SF  35  35.1  24.9  9.6  19.7  48.6  2.0  6.0  33.8  3.7  5.5  67.7  7.9  11.0  1.3  0.5  3.7   28    9  26.10
1   2   Ricky RubioPHX  PG  30  32.0  13.6  4.9  11.9  41.3  1.2  3.7  31.8  2.6  3.1  83.7  4.6   9.3  1.3  0.2  2.5   12    1  16.40
2   3   Luka DoncicDAL  SF  32  32.8  29.7  9.6  20.2  47.5  3.1  9.4  33.1  7.3  9.1  80.5  9.7   8.9  1.2  0.2  4.2   22   11  31.74
3   4   Ben SimmonsPHIL  PG  36  35.4  14.9  6.1  10.8  56.3  0.1  0.1  40.0  2.7  4.6  59.0  7.5   8.6  2.2  0.7  3.6   19    3  19.49
4   5    Trae YoungATL  PG  34  35.1  28.9  9.3  20.8  44.8  3.5  9.4  37.5  6.7  7.9  85.0  4.3   8.4  1.2  0.1  4.8   11    1  23.47

to become this:

print(df.head(5).to_string())
   RK             Name    Team    POS  GP   MIN   PTS  FGM   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  REB   AST  STL  BLK   TO  DD2  TD3    PER
0   1  LeBron James        LA    SF  35  35.1  24.9  9.6  19.7  48.6  2.0  6.0  33.8  3.7  5.5  67.7  7.9  11.0  1.3  0.5  3.7   28    9  26.10
1   2   Ricky Rubio        PHX    PG  30  32.0  13.6  4.9  11.9  41.3  1.2  3.7  31.8  2.6  3.1  83.7  4.6   9.3  1.3  0.2  2.5   12    1  16.40
2   3   Luka Doncic        DAL    SF  32  32.8  29.7  9.6  20.2  47.5  3.1  9.4  33.1  7.3  9.1  80.5  9.7   8.9  1.2  0.2  4.2   22   11  31.74
3   4   Ben Simmons        PHIL    PG  36  35.4  14.9  6.1  10.8  56.3  0.1  0.1  40.0  2.7  4.6  59.0  7.5   8.6  2.2  0.7  3.6   19    3  19.49
4   5    Trae Young        ATL    PG  34  35.1  28.9  9.3  20.8  44.8  3.5  9.4  37.5  6.7  7.9  85.0  4.3   8.4  1.2  0.1  4.8   11    1  23.47

Answer 1

You may extract the data into two columns by using a regex like ^(.*?)([A-Z]+)$ or ^(.*[^A-Z])([A-Z]+)$:

df[['Name','Team']] = df['Name'].str.extract('^(.*?)([A-Z]+)$', expand=True)

This will keep all up to the last char that is not an uppercase letter in Group "Name" and the last uppercase letters in Group "Team".

See regex demo #1 and regex demo #2

Details

• ^ - start of a string
• (.*?) - Capturing group 1: any zero or more chars other than line break chars, as few as possible
  or
  
• (.*[^A-Z]) - any zero or more chars other than line break chars, as many as possible, up to the last char that is not an ASCII uppercase letter (granted the subsequent patterns match) (note that this pattern implies there is at least 1 char before the last uppercase letters)
• ([A-Z]+) - Capturing group 2: one or more ASCII uppercase letters
• $ - end of string.

Answer 2

I have made a few alterations in the functions, You might need to add re package.

Its a bit manual, But I hope this will suffice. Have a great day!

df_obj_skel = dict()
df_obj_skel['Name'] = list()
df_obj_skel['Team'] = list()
for index,row in df.iterrows():
    Name = row['Name']
    Findings = re.search('[A-Z]{2,4}$', Name)
    Refined_Team = Findings[0]
    Refined_Name = re.sub(Refined_Team + "$", "", Name)
    df_obj_skel['Team'].append(Refined_Team)
    df_obj_skel['Name'].append(Refined_Name)
df_final = pd.DataFrame(df_obj_skel)
print(df_final)