大小寫匹配

Question

數據集：

> df
Id       Clean_Data
1918916  Luxury Apartments consisting 11 towers Well equipped gymnasium Swimming Pool Toddler Pool Health Club Steam Room Sauna Jacuzzi Pool Table Chess Billiards room Carom Table Tennis indoor games 
1495638  near medavakkam junction calm area near global hospital
1050651  No Pre Emi No Booking Amount No Floor Rise Charges No Processing Fee HLPROJECT HIGHLIGHTS 

下面的代碼成功地從Category.py的值列表中以ngrams返回匹配的單詞

df['one_word_tokenized_text'] =df["Clean_Data"].str.split()
df['bigram'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 2)))
df['trigram'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 3)))
df['four_words'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 4)))
token=pd.Series(df["one_word_tokenized_text"])
Lid=pd.Series(df["Id"])
matches= token.apply(lambda x: pd.Series(x).str.extractall("|".join(["({})".format(cat) for cat in Categories.HealthCare])))
match_list= [[m for m in match.values.ravel() if isinstance(m, str)] for match in matches]
match_df = pd.DataFrame({"ID":Lid,"jc1":match_list})


def match_word(feature, row):
    categories = []

    for bigram in row.bigram:
        joined = ' '.join(bigram)
        if joined in feature:
            categories.append(joined)
    for trigram in row.trigram:
        joined = ' '.join(trigram)
        if joined in feature:
            categories.append(joined)
    for fourwords in row.four_words:
        joined = ' '.join(fourwords)
        if joined in feature:
            categories.append(joined)
    return categories

match_df['Health1'] = df.apply(partial(match_word, HealthCare), axis=1)
match_df['HealthCare'] = match_df[match_df.columns[[1,2]]].apply(lambda x: ','.join(x.dropna().astype(str)),axis=1)

Category.py

 category = [('steam room','IN','HealthCare'),
        ('sauna','IN','HealthCare'),
        ('Jacuzzi','IN','HealthCare'),
        ('Aerobics','IN','HealthCare'),
        ('yoga room','IN','HealthCare'),]
    HealthCare= [e1 for (e1, rel, e2) in category if e2=='HealthCare']

輸出：

ID  HealthCare
1918916 Jacuzzi
1495638 
1050651 Aerobics, Jacuzzi, yoga room

在這里，如果我在數據集中提到的確切字母大小寫中提到了“類別列表”中的功能，那么代碼將對其進行標識並返回值，否則不會。 因此，我希望我的代碼不區分大小寫，甚至可以在健康類別下跟蹤“ Steam Room”，“桑拿”。 我嘗試使用“ .lower（）”函數，但不確定如何實現。

Answer 1

編輯2：僅category.py被更新

Category.py

category = [('steam room','IN','HealthCare'),
        ('sauna','IN','HealthCare'),
        ('jacuzzi','IN','HealthCare'),
        ('aerobics','IN','HealthCare'),
        ('Yoga room','IN','HealthCare'),
        ('booking','IN','HealthCare'),        
        ]
category1 = [value[0].capitalize() for index, value in enumerate(category)]
category2 = [value[0].lower() for index, value in enumerate(category)]

test = []
test2 =[]

for index, value in enumerate(category1):
    test.append((value, category[index][1],category[index][2])) 

for index, value in enumerate(category2):
    test2.append((value, category[index][1],category[index][2]))

category = category + test + test2


HealthCare = [e1 for (e1, rel, e2) in category if e2=='HealthCare']

您不變的數據集

import pandas as pd
from nltk import ngrams, word_tokenize
import Categories
from Categories import *
from functools import partial


data = {'Clean_Data':['Luxury Apartments consisting 11 towers Well equipped gymnasium Swimming Pool Toddler Pool Health Club Steam Room Sauna Jacuzzi Pool Table Chess Billiards room Carom Table Tennis indoor games',
                     'near medavakkam junction calm area near global hospital',
                     'No Pre Emi No Booking Amount No Floor Rise Charges No Processing Fee HLPROJECT HIGHLIGHTS '],
'Id' : [1918916, 1495638,1050651]}

df = pd.DataFrame(data)


df['one_word_tokenized_text'] =df["Clean_Data"].str.split()
df['bigram'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 2)))
df['trigram'] = df['Clean_Data']).apply(lambda row: list(ngrams(word_tokenize(row), 3)))
df['four_words'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 4)))
token=pd.Series(df["one_word_tokenized_text"])
Lid=pd.Series(df["Id"])
matches= token.apply(lambda x: pd.Series(x).str.extractall("|".join(["({})".format(cat) for cat in Categories.HealthCare])))
match_list= [[m for m in match.values.ravel() if isinstance(m, str)] for match in matches]
match_df = pd.DataFrame({"ID":Lid,"jc1":match_list})


def match_word(feature, row):
    categories = []

    for bigram in row.bigram:
        joined = ' '.join(bigram)
        if joined in feature:
            categories.append(joined)
    for trigram in row.trigram:
        joined = ' '.join(trigram)
        if joined in feature:
            categories.append(joined)
    for fourwords in row.four_words:
        joined = ' '.join(fourwords)
        if joined in feature:
            categories.append(joined)
    return categories

match_df['Health1'] = df.apply(partial(match_word, HealthCare), axis=1)
match_df['HealthCare'] = match_df[match_df.columns[[1,2]]].apply(lambda x: ','.join(x.dropna().astype(str)),axis=1)enize(row), 4)))

產量

print match_df 

+--------+----------------+-------------+------------------------------------+
|ID      |jc1             |Health1      |HealthCare                          |
+--------+----------------+-------------+------------------------------------+
|1918916 |[sauna, jacuzzi]|             |['sauna', 'jacuzzi'],['steam room'] |
+--------+----------------+-------------+------------------------------------+
|1495638 |                |             |                                    |
+--------+----------------+-------------+------------------------------------+
|1050651 |    [Booking]   |             |  ['Booking'],[]                    |                |
+--------+----------------+-------------+------------------------------------+