目前我使用下面的代码使用spaCy对一些文本数据进行lemmatize和计算TF-IDF值：

lemma = []

for doc in nlp.pipe(df['col'].astype('unicode').values, batch_size=9844,
                        n_threads=3):
    if doc.is_parsed:
        lemma.append([n.lemma_ for n in doc if not n.lemma_.is_punct | n.lemma_ != "-PRON-"])
    else:
        lemma.append(None)

df['lemma_col'] = lemma

vect = sklearn.feature_extraction.text.TfidfVectorizer()
lemmas = df['lemma_col'].apply(lambda x: ' '.join(x))
vect = sklearn.feature_extraction.text.TfidfVectorizer()
features = vect.fit_transform(lemmas)

feature_names = vect.get_feature_names()
dense = features.todense()
denselist = dense.tolist()

df = pd.DataFrame(denselist, columns=feature_names)
df = pd.DataFrame(denselist, columns=feature_names)
lemmas = pd.concat([lemmas, df])
df= pd.concat([df, lemmas])

我需要去掉专有名词、标点符号和停用词，但在当前代码中遇到了一些问题。我读了一些documentation和other resources，但现在遇到了一个错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-21-e924639f7822> in <module>()
      7     if doc.is_parsed:
      8         tokens.append([n.text for n in doc])
----> 9         lemma.append([n.lemma_ for n in doc if not n.lemma_.is_punct or n.lemma_ != "-PRON-"])
     10         pos.append([n.pos_ for n in doc])
     11     else:

<ipython-input-21-e924639f7822> in <listcomp>(.0)
      7     if doc.is_parsed:
      8         tokens.append([n.text for n in doc])
----> 9         lemma.append([n.lemma_ for n in doc if not n.lemma_.is_punct or n.lemma_ != "-PRON-"])
     10         pos.append([n.pos_ for n in doc])
     11     else:

AttributeError: 'str' object has no attribute 'is_punct'

有没有更简单的方法把这些东西从文本中剥离出来，而不需要彻底改变我的方法？
完整代码可用here。