как сопоставить частичную строку из текста в pandas кадре данных

Question

Мой фрейм данных выглядит как -

id                               text
1         good,i am interested..please mail me.
2         call me...good to go with you
3         not interested...bye
4         i am not interested don't call me
5         price is too high so not interested
6         i have some requirement..please mail me

Я хочу, чтобы фрейм данных выглядел так -

id                               text                          is_relevant
1         good,i am interested..please mail me.                    yes
2         call me...good to go with you                            yes
3         not interested...bye                                      no
4         i am nt interested don't call me                          no
5         price is too high so not interested                       no
6         i have some requirement..please mail me                   yes

Я сделал следующий код -

d1 = {'no': ['Not interested','nt interested']}
d = {k: oldk for oldk, oldv in d1.items() for k in oldv}
df["is_relevant"] = df['new_text'].map(d).fillna('yes')

Answer 1

Вы можете сделать:

d1 = {'no': ['not interested','nt interested']}

# create regex 
reg = '|'.join([f'\\b{x}\\b' for x in list(d1.values())[0]])

# apply function
df['is_relevant'] = df['text'].str.lower().str.contains(reg).map({True: 'no', False: 'yes'})

   id                                     text is_relevant
0   1    good,i am interested..please mail me.         yes
1   2            call me...good to go with you         yes
2   3                     not interested...bye          no
3   4        i am not interested don't call me          no
4   5      price is too high so not interested          no
5   6  i have some requirement..please mail me         yes
print(df)

Answer 2

In [20]: df = pd.read_csv("a.csv")

In [21]: a
Out[21]: ['not interested', 'nt interested']

In [22]: df
Out[22]:
   id                                     text
0   1    good i am interested..please mail me.
1   2            call me...good to go with you
2   3                     not interested...bye
3   4        i am not interested don't call me
4   5      price is too high so not interested
5   6  i have some requirement..please mail me

In [23]: df["is_relevant"] = df["text"].apply(lambda x: "no" if (a[0] in x.lower() or a[1] in x.lower()) else "yes")

In [24]: df
Out[24]:
   id                                     text is_relevant
0   1    good i am interested..please mail me.         yes
1   2            call me...good to go with you         yes
2   3                     not interested...bye          no
3   4        i am not interested don't call me          no
4   5      price is too high so not interested          no
5   6  i have some requirement..please mail me         yes

Answer 3

Если все, что вам нужно, это то, что находится в списке ['not interested', 'nt interested'].

Если значения находятся в ad dict, отправьте их в список следующим образом lst=list(dict.values()) и по-прежнему np.where

Тогда просто np.where

lst=['not interested', 'nt interested']
df['is_relevant']=np.where(df.text.str.contains("|".join(lst)),'no','yes')

                                     text    is_relevant
1    good,i am interested..please mail me.         yes
2            call me...good to go with you         yes
3                     not interested...bye          no
4        i am not interested don't call me          no
5      price is too high so not interested          no
6  i have some requirement..please mail me         yes

Answer 4

Это похоже на ответ YOLO выше, но позволяет использовать несколько текстовых классов.

df = pd.DataFrame(
    data = ["good,i am interested..please mail me.",
            "call me...good to go with you",
            "not interested...bye",
            "i am not interested don't call me",
            "price is too high so not interested",
            "i have some requirement..please mail me"],
    columns=['text'], index=[1,2,3,4,5,6])

d1 = {'no': ['Not interested','nt interested','not interested'],
      'maybe': ['requirement']}
df['is_relevant'] = 'yes'

for k in d1:
    match_inds = reduce(lambda x,y: x | y,
                        [df['text'].str.contains(pat) for pat in d1[k]])
    df.loc[match_inds, 'is_relevant'] = k

print(df)

Вывод

   text                                    is_relevant
1    good,i am interested..please mail me.         yes
2            call me...good to go with you         yes
3                     not interested...bye          no
4        i am not interested don't call me          no
5      price is too high so not interested          no
6  i have some requirement..please mail me       maybe