I have a data frame which contains a text column i.e. df["input"]
,
I would like to create a new variable which checks whether df["input"]
column contains any of the word in a given list and assigns a value of 1 if previous dummy variable is equal to 0 (logic is 1) create a dummy variable that equals to zero 2) replace it to one if it contains any word in a given list and it was not contained in the previous lists.)
# Example lists
listings = ["amazon listing", "ecommerce", "products"]
scripting = ["subtitle", "film", "dubbing"]
medical = ["medical", "biotechnology", "dentist"]
df = pd.DataFrame({'input': ['amazon listing subtitle',
'medical',
'film biotechnology dentist']})
which looks like:
input
amazon listing subtitle
medical
film biotechnology dentist
final dataset should look like:
input listings scripting medical
amazon listing subtitle 1 0 0
medical 0 0 1
film biotechnology dentist 0 1 0
CodePudding user response:
One possible implementation is to use str.contains
in a loop to create the 3 columns, then use idxmax
to get the column name (or the list name) of the first match, then create a dummy variable from these matches:
import numpy as np
d = {'listings':listings, 'scripting':scripting, 'medical':medical}
for k,v in d.items():
df[k] = df['input'].str.contains('|'.join(v))
arr = df[list(d)].to_numpy()
tmp = np.zeros(arr.shape, dtype='int8')
tmp[np.arange(len(arr)), arr.argmax(axis=1)] = arr.max(axis=1)
out = pd.DataFrame(tmp, columns=list(d)).combine_first(df)
But in this case, it might be more efficient to use a nested for-loop:
import re
def get_dummy_vars(col, lsts):
out = []
len_lsts = len(lsts)
for row in col:
tmp = []
# in the nested loop, we use the any function to check for the first match
# if there's a match, break the loop and pad 0s since we don't care if there's another match
for lst in lsts:
tmp.append(int(any(True for x in lst if re.search(fr"\b{x}\b", row))))
if tmp[-1]:
break
tmp = [0] * (len_lsts - len(tmp))
out.append(tmp)
return out
lsts = [listings, scripting, medical]
out = df.join(pd.DataFrame(get_dummy_vars(df['input'], lsts), columns=['listings', 'scripting', 'medical']))
Output:
input listings medical scripting
0 amazon listing subtitle 1 0 0
1 medical 0 1 0
2 film biotechnology dentist 0 0 1