text = re.sub("<.*?>", " ", text, flags=re.S) text = re.sub("|".join(fileters), " ", text, flags=re.S)
时间: 2023-12-04 11:03:44 浏览: 33
这段代码使用了Python的正则表达式库re,对文本数据进行了处理。具体地说,它使用了两个re.sub()函数。
第一个函数使用了正则表达式 "<.*?>",匹配了所有的HTML标签,并使用空格替换了这些标签,从而将HTML标签从文本中去除。
第二个函数使用了一个由fileters列表中的元素拼接成的正则表达式,匹配了所有fileters列表中出现的单词,并使用空格替换了这些单词。这个正则表达式中使用了 "|" 符号,表示匹配其中任意一个单词。这样,我们可以将一些无意义的单词或符号从文本中去除,从而提高文本数据的质量。
需要注意的是,这段代码中的text变量需要先定义并赋值。同时,fileters是一个列表,其中存放的是需要过滤的单词或符号。例如,fileters = ['a', 'an', 'the', 'is', 'are', 'in', 'on'],表示需要去除这些单词。
相关问题
优化代码 def fault_classification_wrapper(vin, main_path, data_path, log_path, done_path): start_time = time.time() isc_path = os.path.join(done_path, vin, 'isc_cal_result', f'{vin}_report.xlsx') if not os.path.exists(isc_path): print('No isc detection input!') else: isc_input = isc_produce_alarm(isc_path, vin) ica_path = os.path.join(done_path, vin, 'ica_cal_result', f'ica_detection_alarm_{vin}.csv') if not os.path.exists(ica_path): print('No ica detection input!') else: ica_input = ica_produce_alarm(ica_path) soh_path = os.path.join(done_path, vin, 'SOH_cal_result', f'{vin}_sohAno.csv') if not os.path.exists(soh_path): print('No soh detection input!') else: soh_input = soh_produce_alarm(soh_path, vin) alarm_df = pd.concat([isc_input, ica_input, soh_input]) alarm_df.reset_index(drop=True, inplace=True) alarm_df['alarm_cell'] = alarm_df['alarm_cell'].apply(lambda _: str(_)) print(vin) module = AutoAnalysisMain(alarm_df, main_path, data_path, done_path) module.analysis_process() flags = os.O_WRONLY | os.O_CREAT modes = stat.S_IWUSR | stat.S_IRUSR with os.fdopen(os.open(os.path.join(log_path, 'log.txt'), flags, modes), 'w') as txt_file: for k, v in module.output.items(): txt_file.write(k + ':' + str(v)) txt_file.write('\n') for x, y in module.output_sub.items(): txt_file.write(x + ':' + str(y)) txt_file.write('\n\n') fc_result_path = os.path.join(done_path, vin, 'fc_result') if not os.path.exists(fc_result_path): os.makedirs(fc_result_path) pd.DataFrame(module.output).to_csv( os.path.join(fc_result_path, 'main_structure.csv')) df2 = pd.DataFrame() for subs in module.output_sub.keys(): sub_s = pd.Series(module.output_sub[subs]) df2 = df2.append(sub_s, ignore_index=True) df2.to_csv(os.path.join(fc_result_path, 'sub_structure.csv')) end_time = time.time() print("time cost of fault classification:", float(end_time - start_time) * 1000.0, "ms") return
Here are some suggestions to optimize the code:
1. Use list comprehension to simplify the code:
```
alarm_df = pd.concat([isc_input, ica_input, soh_input]).reset_index(drop=True)
alarm_df['alarm_cell'] = alarm_df['alarm_cell'].apply(str)
```
2. Use context manager to simplify file operation:
```
with open(os.path.join(log_path, 'log.txt'), 'w') as txt_file:
for k, v in module.output.items():
txt_file.write(f"{k}:{v}\n")
for x, y in module.output_sub.items():
txt_file.write(f"{x}:{y}\n\n")
```
3. Use `Pathlib` to simplify path operation:
```
fc_result_path = Path(done_path) / vin / 'fc_result'
fc_result_path.mkdir(parents=True, exist_ok=True)
pd.DataFrame(module.output).to_csv(fc_result_path / 'main_structure.csv')
pd.DataFrame(module.output_sub).to_csv(fc_result_path / 'sub_structure.csv')
```
4. Use f-string to simplify string formatting:
```
print(f"time cost of fault classification: {(end_time - start_time) * 1000.0} ms")
```
Here's the optimized code:
```
def fault_classification_wrapper(vin, main_path, data_path, log_path, done_path):
start_time = time.time()
isc_path = Path(done_path) / vin / 'isc_cal_result' / f'{vin}_report.xlsx'
if not isc_path.exists():
print('No isc detection input!')
isc_input = pd.DataFrame()
else:
isc_input = isc_produce_alarm(isc_path, vin)
ica_path = Path(done_path) / vin / 'ica_cal_result' / f'ica_detection_alarm_{vin}.csv'
if not ica_path.exists():
print('No ica detection input!')
ica_input = pd.DataFrame()
else:
ica_input = ica_produce_alarm(ica_path)
soh_path = Path(done_path) / vin / 'SOH_cal_result' / f'{vin}_sohAno.csv'
if not soh_path.exists():
print('No soh detection input!')
soh_input = pd.DataFrame()
else:
soh_input = soh_produce_alarm(soh_path, vin)
alarm_df = pd.concat([isc_input, ica_input, soh_input]).reset_index(drop=True)
alarm_df['alarm_cell'] = alarm_df['alarm_cell'].apply(str)
print(vin)
module = AutoAnalysisMain(alarm_df, main_path, data_path, done_path)
module.analysis_process()
with open(Path(log_path) / 'log.txt', 'w') as txt_file:
for k, v in module.output.items():
txt_file.write(f"{k}:{v}\n")
for x, y in module.output_sub.items():
txt_file.write(f"{x}:{y}\n\n")
fc_result_path = Path(done_path) / vin / 'fc_result'
fc_result_path.mkdir(parents=True, exist_ok=True)
pd.DataFrame(module.output).to_csv(fc_result_path / 'main_structure.csv')
pd.DataFrame(module.output_sub).to_csv(fc_result_path / 'sub_structure.csv')
end_time = time.time()
print(f"time cost of fault classification: {(end_time - start_time) * 1000.0} ms")
return
```
import jieba from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np import pandas as pd # 读取停用词文件 def read_stopwords(file_path): with open(file_path, 'r', encoding='gbk') as f: stopwords = [line.strip() for line in f] return set(stopwords) # 中文分词 def chinese_word_cut(text, stopwords): words = jieba.cut(text) result = [] for word in words: if word not in stopwords: result.append(word) return " ".join(result) # 读取CSV文件 weibo_data = pd.read_csv('E:\Python自然语言处理\data\weibo_Convid19.csv', sep='\t') df = weibo_data['text_raw'] # 获取停用词集合 stopwords = read_stopwords('E:\Python自然语言处理\data\stopword.txt') # 对每条微博进行分词和去停用词 corpus_list = df.apply(lambda x: chinese_word_cut(x, stopwords)) # 提取关键词 corpus = ' '.join(corpus_list) tfidf = TfidfVectorizer() tf_key = tfidf.fit_transform([corpus]) word = tfidf.get_feature_names() weight = tf_key.toarray()[0] w_sort = np.argsort(-weight) print('Top 20 keywords:') for i in range(20): print(word[w_sort[i]])结果含有表情包,怎么去除
可以使用正则表达式来去除文本中的表情包,例如:
```
import re
def remove_emoji(text):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
"]+", flags=re.UNICODE)
return emoji_pattern.sub(r'', text)
# 在 chinese_word_cut 函数中调用 remove_emoji 函数
def chinese_word_cut(text, stopwords):
text = remove_emoji(text)
words = jieba.cut(text)
result = []
for word in words:
if word not in stopwords:
result.append(word)
return " ".join(result)
```
这样就可以在分词前去除文本中的表情包。