代码 :

data = pd.read_csv('asscsv2.csv', encoding = "ISO-8859-1", error_bad_lines=False);
data_text = data[['content']]
data_text['index'] = data_text.index
documents = data_text

输出

print(documents[:2])
                                              content  index
 0  Pretty extensive background in Egyptology and ...      0
 1  Have you guys checked the back end of the Sphi...      1

预处理函数

stemmer = PorterStemmer()
def lemmatize_stemming(text):
    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))
def preprocess(text):
    result = []
    for token in gensim.utils.simple_preprocess(text):
        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:
            result.append(lemmatize_stemming(token))
    return result

processed_docs = documents['content'].map(preprocess)

报错

TypeError: decoding to str: need a bytes-like object, float found

This :

processed_docs = documents['content'].map(preprocess)

is because the data frame in some cells has NaN values that can not be preprocessed, for that, you have to drop:

documents.dropna(subset = ["content"], inplace=True) # drop those rows which have NaN value cells

those unrequired rows and then apply the preprocessing.

Your data has NaNs(not a number).

You can either drop them first:

documents = documents.dropna(subset=['content'])

Or, you can fill all NaNs with an empty string, convert the column to string type and then map your string based function.

documents['content'].fillna('').astype(str).map(preprocess)

This is because your function preprocess has function calls that accept string only data type.