I am trying to clean a set of strings to remove unwanted characters.
Input
Lethal Lunch t5+ 0 0 D 10 t5+ Michael Bell . Alex Jary7 .
Muscika 1 v5+ W5+ 0 0 D 5 v5+ W5+ D O'Meara . Cam Hardie . C5
Typhoon Ten 1 0 0 D 13 R Hannon . Luke Catton7 .
Wentworth Falls 1 cp5+ 0 0 C D 45 cp5+ G Harker . Connor Beasley .
One Night Stand 0 0 D 34 W Jarvis . Silvestre De Sousa . 30 C1 C5
Dancinginthewoods 1 0 0 D 24 D Ivory . 14 Jamie Spencer . 30
Case Key 1 v3 0 0 D 13 v3 M Appleby . Andrew Mullen . 14
Wanted Output
Lethal Lunch
Muscika
Typhoon Ten
Wentworth Falls
One Night Stand
Dancinginthewoods
Case Key
I have tried this
re.findall('([a-zA-Z ]*)\d*.*',final_df.loc[index, 'Horse'])
This removes everything after a number but it leaves the t on the first entry. I was wondering if there is a better way?
I'd use re.split instead:
for d in data.splitlines():
print(re.split(r'\s+t?[0-9]\+?', d)[0])
Lethal Lunch
Muscika
Typhoon Ten
Wentworth Falls
One Night Stand
Dancinginthewoods
Case Key
Explanation: It splits the string at places where the specified pattern matches, then takes the first part. You probably want to tweak it so that other patterns also match.
I just noticed you seem to be using Pandas – assuming your df looks like this:
Horse
0 Lethal Lunch t5+ 0 0 D 10 t5+ Michael Bell . A...
1 Muscika 1 v5+ W5+ 0 0 D 5 v5+ W5+ D O'Meara . ...
2 Typhoon Ten 1 0 0 D 13 R Hannon . Luke Catton7 .
3 Wentworth Falls 1 cp5+ 0 0 C D 45 cp5+ G Harke...
4 One Night Stand 0 0 D 34 W Jarvis . Silvestre ...
5 Dancinginthewoods 1 0 0 D 24 D Ivory . 14 Jami...
6 Case Key 1 v3 0 0 D 13 v3 M Appleby . Andrew M...
You can do
from operator import itemgetter
df["name"] = df.Horse.str.split('\s+t?[0-9]\+?').map(itemgetter(0))
to get this:
Horse name
0 Lethal Lunch t5+ 0 0 D 10 t5+ Michael Bell . A... Lethal Lunch
1 Muscika 1 v5+ W5+ 0 0 D 5 v5+ W5+ D O'Meara . ... Muscika
2 Typhoon Ten 1 0 0 D 13 R Hannon . Luke Catton7 . Typhoon Ten
3 Wentworth Falls 1 cp5+ 0 0 C D 45 cp5+ G Harke... Wentworth Falls
4 One Night Stand 0 0 D 34 W Jarvis . Silvestre ... One Night Stand
5 Dancinginthewoods 1 0 0 D 24 D Ivory . 14 Jami... Dancinginthewoods
6 Case Key 1 v3 0 0 D 13 v3 M Appleby . Andrew M... Case Key