I have the following data, having two columns, "title name" and "gross" in table called train_df:
gross title name
760507625.0 Avatar (2009)
658672302.0 Titanic (1997)
652270625.0 Jurassic World (2015)
623357910.0 The Avengers (2012)
534858444.0 The Dark Knight (2008)
532177324.0 Rogue One (2016)
474544677.0 Star Wars: Episode I - The Phantom Menace (1999)
459005868.0 Avengers: Age of Ultron (2015)
448139099.0 The Dark Knight Rises (2012)
436471036.0 Shrek 2 (2004)
424668047.0 The Hunger Games: Catching Fire (2013)
423315812.0 Pirates of the Caribbean: Dead Man's Chest (2006)
415004880.0 Toy Story 3 (2010)
409013994.0 Iron Man 3 (2013)
408084349.0 Captain America: Civil War (2016)
408010692.0 The Hunger Games (2012)
403706375.0 Spider-Man (2002)
402453882.0 Jurassic Park (1993)
402111870.0 Transformers: Revenge of the Fallen (2009)
400738009.0 Frozen (2013)
381011219.0 Harry Potter and the Deathly Hallows: Part 2 (2011)
380843261.0 Finding Nemo (2003)
380262555.0 Star Wars: Episode III - Revenge of the Sith (2005)
373585825.0 Spider-Man 2 (2004)
370782930.0 The Passion of the Christ (2004)
I would like to remove the date from "title name". Output should look as follows:
gross title name
760507625.0 Avatar
658672302.0 Titanic
652270625.0 Jurassic World
623357910.0 The Avengers
534858444.0 The Dark Knight
Ignore the gross column as it needs no changing.
CodePudding user response：
Using str.replace we can try:
train_df["title name"] = train_df["title name"].str.replace(r'\s \(\d{4}\)$', '', regex=True)
CodePudding user response：
Another solution, without re and only using .str.rsplit():
df['title name'] = df['title name'].str.rsplit(' (', n=1).str[0]
print(df)
Prints:
gross title name
0 760507625.0 Avatar
1 658672302.0 Titanic
2 652270625.0 Jurassic World
3 623357910.0 The Avengers
4 534858444.0 The Dark Knight
5 532177324.0 Rogue One
6 474544677.0 Star Wars: Episode I - The Phantom Menace
7 459005868.0 Avengers: Age of Ultron
8 448139099.0 The Dark Knight Rises
9 436471036.0 Shrek 2
10 424668047.0 The Hunger Games: Catching Fire
11 423315812.0 Pirates of the Caribbean: Dead Man's Chest
12 415004880.0 Toy Story 3
13 409013994.0 Iron Man 3
14 408084349.0 Captain America: Civil War
15 408010692.0 The Hunger Games
16 403706375.0 Spider-Man
17 402453882.0 Jurassic Park
18 402111870.0 Transformers: Revenge of the Fallen
19 400738009.0 Frozen
20 381011219.0 Harry Potter and the Deathly Hallows: Part 2
21 380843261.0 Finding Nemo
22 380262555.0 Star Wars: Episode III - Revenge of the Sith
23 373585825.0 Spider-Man 2
24 370782930.0 The Passion of the Christ