>AttributeError: 'list' object has no attribute 'lower' (in a lowercase dataframe)

>AttributeError: 'list' object has no attribute 'lower' (in a lowercase dataframe)

我不明白这个错误...我在把它变成列表之前已经把df变成了小写

数据框:

    all_cols
0   who is your hero and why
1   what do you do to relax
2   this is a hero
4   how many hours of sleep do you get a night
5   describe the last time you were relax

代码:

from sklearn.cluster import MeanShift
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.feature_extraction.text import TfidfVectorizer

df['all_cols'] = df['all_cols'].str.lower()
df_list = df.values.tolist()

pipeline = Pipeline(steps=[
('tfidf', TfidfVectorizer()),
('trans', FunctionTransformer(lambda x: x.todense(), accept_sparse=True)),
('clust', MeanShift())])

pipeline.fit(df_list)
pipeline.named_steps['clust'].labels_

result = [(label,doc) for doc,label in zip(df_list, pipeline.named_steps['clust'].labels_)]

for label,doc in sorted(result):
    print(label, doc)

但是我在这一行有一个错误:

AttributeError Traceback (most recent call last) in

----> 1 pipeline.fit(df_list)

 2 pipeline.named_steps['clust'].labels_

AttributeError: 'list' object has no attribute 'lower'

但是,如果我之前已经传递了小写数据帧,为什么程序会返回小写错误?

df_list 指定的列以避免嵌套列表:

df_list = df.values.tolist()
print (df_list)
[['who is your hero and why'], 
 ['what do you do to relax'], 
 ['this is a hero'], 
 ['how many hours of sleep do you get a night'], 
 ['describe the last time you were relax']]

df_list = df['all_cols'].values.tolist()
print (df_list)
['who is your hero and why', 
 'what do you do to relax', 
 'this is a hero',
 'how many hours of sleep do you get a night',
 'describe the last time you were relax']

将其转换为pandas数据框,然后进行上述操作。它会起作用。 我仍然粘贴了代码片段,你可以自己试试。

import pandas as pd

col = pd.Series(["who is your hero and why", "what do you do to relax", "this is a hero", "how many hours of sleep do you get a night", "describe the last time you were relax"])
fr = {"all_cols":col}
df = pd.DataFrame(fr)
df['all_cols'] = df['all_cols'].str.lower()
df_list = df.values.tolist()