Reputation: 13
I have a given list of strings, like that:
List=['plastic', 'carboard', 'wood']
I have a column of dtype string in my dataframe, like that:
Column=['beer plastic', 'water cardboard', 'eggs plastic', 'fruits wood']
For each row in the column, I want to know if the row contains a word from the list, and if yes, I want to keep only the text that comes before that word, like that:
New_Column=['beer', 'water', 'eggs', 'fruits']
Is there a way to systematize this for each row of my dataframe (millions of rows)? Thanks
PS. I have tried building a function with regular expression pattern matching like this
pattern=re.compile('**Pattern to be defined to include element from list**')
def truncate(row, pattern):
Column=row['Column']
if bool(pattern.match(Column)):
Column=Column.replace(**word from list**,"")
return Column
df['New_column']=df.apply(truncate,axis=1, pattern=pattern)
Upvotes: 1
Views: 7607
Reputation: 111
import pandas as pd
...
for index, row in df.iterrows():
for word in List_name:
row['Column_name'] = row['Column_name'].partition(word)[0] if (word in row['Column_name']) else row['Column_name']
If you want to run a working example:
import pandas as pd
List=['plastic', 'carboard', 'wood']
df = pd.DataFrame([{'c1':"fun carboard", 'c2':"jolly plastic"}, {'c1':"meh wood",'c2':"aba"}, {'c1':"aaa",'c2':"bbb"}, {'c1':"old wood",'c2':"bbb"}])
for index, row in df.iterrows():
for word in List:
row['c1'] = row['c1'].partition(word)[0] if (word in row['c1']) else row['c1']
row['c2'] = row['c2'].partition(word)[0] if (word in row['c2']) else row['c2']
df
Upvotes: 1
Reputation: 3770
##df
0
0 beer plastic
1 water cardboard
2 eggs plastic
3 fruits wood
l=['plastic', 'cardboard', 'wood']
str.findall
df[0].str.findall('\w+\s*(?=' + '|'.join(l) +')').apply(lambda x: x[0].strip() if len(x) else 'NotFound')
##output
0 beer
1 water
2 eggs
3 fruits
Name: 0, dtype: object
Upvotes: 2