问题描述
当我在 Dataframe 上方运行时，它显示错误字符串索引必须是整数。我不知道如何解决这个问题。
这是我迄今为止尝试过的代码
# Dictionary of English contractions
contractions_dict = {"ain't": "are not","'s":" is","aren't": "are not","can't": "cannot","can't've": "cannot have","'cause": "because","Could've": "Could have","Couldn't": "Could not","Couldn't've": "Could not have","didn't": "did not","doesn't": "does not","don't": "do not","hadn't": "had not","hadn't've": "had not have","hasn't": "has not","haven't": "have not","he'd": "he would","he'd've": "he would have","he'll": "he will","he'll've": "he will have","how'd": "how did","how'd'y": "how do you","how'll": "how will","I'd": "I would","I'd've": "I would have","I'll": "I will","I'll've": "I will have","I'm": "I am","I've": "I have","isn't": "is not","it'd": "it would","it'd've": "it would have","it'll": "it will","it'll've": "it will have","let's": "let us","ma'am": "madam","mayn't": "may not","might've": "might have","mightn't": "might not","mightn't've": "might not have","must've": "must have","mustn't": "must not","mustn't've": "must not have","needn't": "need not","needn't've": "need not have","o'clock": "of the clock","oughtn't": "ought not","oughtn't've": "ought not have","shan't": "shall not","sha'n't": "shall not","shan't've": "shall not have","she'd": "she would","she'd've": "she would have","she'll": "she will","she'll've": "she will have","should've": "should have","shouldn't": "should not","shouldn't've": "should not have","so've": "so have","that'd": "that would","that'd've": "that would have","there'd": "there would","there'd've": "there would have","they'd": "they would"}
# Regular expression for finding contractions
contractions_re=re.compile('(%s)' % '|'.join(contractions_dict.keys()))
# Function for expanding contractions
def expand_contractions(text,contractions_dict=contractions_dict):
def replace(match):
return contractions_dict[match.group(0)]
# Expanding contractions in the reviews
dataset['entitas bernama']=dataset['entitas bernama'].apply(lambda x:expand_contractions(x))
这是错误
dataset['entitas bernama']=dataset['entitas bernama'].apply(lambda x:expand_contractions(x))
error : string indices must be integers
解决方法
这是在熊猫中替换系列值的方法
pandas.Series.replace(to_replace=contractions_dict,inplace=True,value=None,regex=True)
来自https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.replace.html：
Dicts 可用于为不同的现有值指定不同的替换值。例如，{'a': 'b','y': 'z'} 将值 'a' 替换为 'b'，将 'y' 替换为 'z'。要以这种方式使用字典，value 参数应为 None。
示例
contraction_dict = {...} # redacted
In []: twt = pd.read_csv('twitter4000.csv')
Out[]:
tweets sentiment
0 is bored and wants to watch a movie any sugge... 0
1. back in miami. waiting to unboard ship 0
2 @misskpey awwww dnt dis brng bak memoriessss,... 0
3 ughhh i am so tired blahhhhhhhhh 0
4 @mandagoforth me bad! It's funny though. Zacha... 0
... ... ...
3995 i just graduated 1
3996 Templating works; it all has to be done 1
3997 mommy just brought me starbucks 1
3998 @omarepps watching you on a House re-run...lov... 1
3999 Thanks for trying to make me smile I'll make y... 1
4000 rows × 2 columns
# notice in a glance only the last row has contraction in head +5 tail -5
In []: # check which rows has contractions
twt[twt.tweets.str.contains('|'.join(contractions_dict.keys()),regex=True)]
Out[]:
tweets sentiment
2 @misskpey awwww dnt dis brng bak memoriessss,... 0
4 @mandagoforth me bad! It's funny though. Zacha... 0
5 brr,i'm so cold. at the moment doing my assig... 0
6 @kevinmarquis haha yep but i really need to sl... 0
7 eating some ice-cream while I try to see @pete... 0
... ... ...
3961 gonna cousin's b.day. 1
3968 @kat_n Got to agree it's a risk to put her thr... 1
3983 About to watch the Lakers win game duece. I'm ... 1
3986 @countroshculla yeah..needed to get up early..... 1
3999 Thanks for trying to make me smile I'll make y... 1
937 rows × 2 columns
In []: twt.tail(5).tweets.replace(to_replace=contractions_dict,regex=True)
Out[]:
3995 i just graduated
3996 Templating works; it all has to be done
3997 mommy just brought me starbucks
3998 @omarepps watching you on a House re-run...lov...
3999 Thanks for trying to make me smile I will make...
Name: tweets,dtype: object
对 inplace=True 使用参数 Series.replace 以避免分配回 df 即 twt.tweets = twt.tweets.replace(...)